Content summarization for assistant systems

ABSTRACT

In one embodiment, a method includes, by one or more computing systems, receiving, from a client system associated with a user, a request for a summary of user communications from a content source, accessing a plurality of user communications from the content source, identifying a plurality of segments associated with the plurality of user communications, wherein the plurality of segments is associated with a plurality of topics, respectively, calculating, for each segment of the plurality of segments, a user interest score for the segment, selecting one or more of the segments for summarization based on their user interest scores, generating one or more personalized summaries of the one or more selected segments, wherein the personalization of the summary is based on the user profile of the user and sending, to the client system, instructions to present the personalized summaries to the user responsive to the request.

PRIORITY

This application claims the benefit, under 35 U.S.C. § 119(e), of U.S.Provisional Patent Application No. 62/660,876, filed 20 Apr. 2018, whichis incorporated herein by reference.

TECHNICAL FIELD

This disclosure generally relates to databases and file managementwithin network environments, and in particular relates to hardware andsoftware for smart assistant systems.

BACKGROUND

An assistant system can provide information or services on behalf of auser based on a combination of user input, location awareness, and theability to access information from a variety of online sources (such asweather conditions, traffic congestion, news, stock prices, userschedules, retail prices, etc.). The user input may include text (e.g.,online chat), especially in an instant messaging application or otherapplications, voice, images, motion, or a combination of them. Theassistant system may perform concierge-type services (e.g., makingdinner reservations, purchasing event tickets, making travelarrangements) or provide information based on the user input. Theassistant system may also perform management or data-handling tasksbased on online information and events without user initiation orinteraction. Examples of those tasks that may be performed by anassistant system may include schedule management (e.g., sending an alertto a dinner date that a user is running late due to traffic conditions,update schedules for both parties, and change the restaurant reservationtime). The assistant system may be enabled by the combination ofcomputing devices, application programming interfaces (APIs), and theproliferation of applications on user devices.

A social-networking system, which may include a social-networkingwebsite, may enable its users (such as persons or organizations) tointeract with it and with each other through it. The social-networkingsystem may, with input from a user, create and store in thesocial-networking system a user profile associated with the user. Theuser profile may include demographic information, communication-channelinformation, and information on personal interests of the user. Thesocial-networking system may also, with input from a user, create andstore a record of relationships of the user with other users of thesocial-networking system, as well as provide services (e.g. profile/newsfeed posts, photo-sharing, event organization, messaging, games, oradvertisements) to facilitate social interaction between or among users.

The social-networking system may send over one or more networks contentor messages related to its services to a mobile or other computingdevice of a user. A user may also install software applications on amobile or other computing device of the user for accessing a userprofile of the user and other data within the social-networking system.The social-networking system may generate a personalized set of contentobjects to display to a user, such as a newsfeed of aggregated storiesof other users connected to the user.

SUMMARY OF PARTICULAR EMBODIMENTS

In particular embodiments, the assistant system may assist a user toobtain information or services. The assistant system may enable the userto interact with it with multi-modal user input (such as voice, text,image, video, motion) in stateful and multi-turn conversations to getassistance. The assistant system may create and store a user profilecomprising both personal and contextual information associated with theuser. In particular embodiments, the assistant system may analyze theuser input using natural-language understanding. The analysis may bebased on the user profile for more personalized and context-awareunderstanding. The assistant system may resolve entities associated withthe user input based on the analysis. In particular embodiments, theassistant system may interact with different agents to obtaininformation or services that are associated with the resolved entities.The assistant system may generate a response for the user regarding theinformation or services by using natural-language generation. Throughthe interaction with the user, the assistant system may use dialogmanagement techniques to manage and forward the conversation flow withthe user. In particular embodiments, the assistant system may furtherassist the user to effectively and efficiently digest the obtainedinformation by summarizing the information. The assistant system mayalso assist the user to be more engaging with an online social networkby providing tools that help the user interact with the online socialnetwork (e.g., creating posts, comments, messages). The assistant systemmay additionally assist the user to manage different tasks such askeeping track of events. In particular embodiments, the assistant systemmay proactively execute tasks that are relevant to user interests andpreferences based on the user profile without a user input. Inparticular embodiments, the assistant system may check privacy settingsto ensure that accessing a user's profile or other user information andexecuting different tasks are permitted subject to the user's privacysettings.

In particular embodiment, the assistant system may summarize content topresent to a user. Users may periodically pause in their consumption ofmedia whether it be emails, group chats, interacting with his or heronline social network, and the like. The pause may be the result of auser taking a vacation (e.g., pause in work emails), taking a break fromsocial media (e.g., pause in social networking activity), and the like.The pause in checking content, such as user communications (e.g.,electronic communications, messages, and the like), may create a backlogof content that the user may want to address or view at a later date. Toavoid the time-consuming task of reading through every single contentitem, the assistant system may provide a summarization of content (e.g.,user communications, such as text messages, emails, posts on an onlinesocial network, etc.) the user has missed. As an example and not by wayof limitation, the user may ask, “Hey Assistant, summarize my unreadwork emails,” and the assistant system may generate a summary of thework emails that the user has missed. To generate the summary of workemails, the assistant system may identify separate email threads tosummarize and provide the separate summaries of each segment to theuser. In particular embodiments, in order to invoke the summarization,the user may perform an action to request the assistant system togenerate a summary of a specific content source (e.g., within anelectronic communications inbox, a message thread, a newsfeed, etc.) theuser is in. As an example and not by way of limitation, the user mayinput a request into a composer interface for a summary of missedactivity. The assistant system may use a user profile of the requestinguser to identify content the user is most interested in and prioritizethat content to summarize for the user to view. After the assistantsystem compiles the content, the assistant system may send thesummarized content for the user to view. The summarized content may bepresented in any suitable modality (e.g., text/images, or audio/video ona client system). Although this disclosure describes summarizing contentby an assistant system to present to a user in a particular manner, thisdisclosure contemplates summarizing content by an assistant system topresent to a user in any suitable manner.

In particular embodiments, the assistant system may generate summariesfor a content source. In particular embodiments, the assistant systemmay receive a request for a summary of user communications from a firstcontent source from a client system associated with a first user. Thefirst user may be a user of an online social network. In particularembodiments, the assistant system may access a plurality of usercommunications from the first content source. In particular embodiments,the assistant system may identify a plurality of segments associatedwith the plurality of user communications. The plurality of segments maybe associated with a plurality of topics, respectively. In particularembodiments, the assistant system may calculate, for each segment of theplurality of segments, a user interest score for the segment. The userinterest score may be based on a user profile of the first user. Inparticular embodiments, the assistant system may select one or more ofthe segments for summarization based on their user interest scores. Inparticular embodiments, the assistant system may generate one or morepersonalized summaries of the one or more selected segments. Thepersonalization of the summary may be based on the user profile of thefirst user. In particular embodiments, the assistant system may sendinstructions to present the personalized summaries to the first userresponsive to the request.

The embodiments disclosed herein are only examples, and the scope ofthis disclosure is not limited to them. Particular embodiments mayinclude all, some, or none of the components, elements, features,functions, operations, or steps of the embodiments disclosed herein.Embodiments according to the invention are in particular disclosed inthe attached claims directed to a method, a storage medium, a system anda computer program product, wherein any feature mentioned in one claimcategory, e.g. method, can be claimed in another claim category, e.g.system, as well. The dependencies or references back in the attachedclaims are chosen for formal reasons only. However any subject matterresulting from a deliberate reference back to any previous claims (inparticular multiple dependencies) can be claimed as well, so that anycombination of claims and the features thereof are disclosed and can beclaimed regardless of the dependencies chosen in the attached claims.The subject-matter which can be claimed comprises not only thecombinations of features as set out in the attached claims but also anyother combination of features in the claims, wherein each featurementioned in the claims can be combined with any other feature orcombination of other features in the claims. Furthermore, any of theembodiments and features described or depicted herein can be claimed ina separate claim and/or in any combination with any embodiment orfeature described or depicted herein or with any of the features of theattached claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example network environment associated with anassistant system.

FIG. 2 illustrates an example architecture of the assistant system.

FIG. 3 illustrates an example diagram flow of responding to a userrequest by the assistant system.

FIG. 4 illustrates an example diagram flow of generating summaries by anassistant system for a content source.

FIGS. 5A-5C illustrate example user interfaces and back end processduring generation of summaries for a content source.

FIGS. 6A-6B illustrate an example process of segmenting usercommunications.

FIG. 7 illustrates an example method for generating summaries by anassistant system for a content source.

FIG. 8 illustrates an example social graph.

FIG. 9 illustrates an example view of an embedding space.

FIG. 10 illustrates an example artificial neural network.

FIG. 11 illustrates an example computer system.

DESCRIPTION OF EXAMPLE EMBODIMENTS

System Overview

FIG. 1 illustrates an example network environment 100 associated with anassistant system. Network environment 100 includes a client system 130,an assistant system 140, a social-networking system 160, and athird-party system 170 connected to each other by a network 110.Although FIG. 1 illustrates a particular arrangement of a client system130, an assistant system 140, a social-networking system 160, athird-party system 170, and a network 110, this disclosure contemplatesany suitable arrangement of a client system 130, an assistant system140, a social-networking system 160, a third-party system 170, and anetwork 110. As an example and not by way of limitation, two or more ofa client system 130, a social-networking system 160, an assistant system140, and a third-party system 170 may be connected to each otherdirectly, bypassing a network 110. As another example, two or more of aclient system 130, an assistant system 140, a social-networking system160, and a third-party system 170 may be physically or logicallyco-located with each other in whole or in part. Moreover, although FIG.1 illustrates a particular number of client systems 130, assistantsystems 140, social-networking systems 160, third-party systems 170, andnetworks 110, this disclosure contemplates any suitable number of clientsystems 130, assistant systems 140, social-networking systems 160,third-party systems 170, and networks 110. As an example and not by wayof limitation, network environment 100 may include multiple clientsystems 130, assistant systems 140, social-networking systems 160,third-party systems 170, and networks 110.

This disclosure contemplates any suitable network 110. As an example andnot by way of limitation, one or more portions of a network 110 mayinclude an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), a portion of the Internet, a portion of the Public SwitchedTelephone Network (PSTN), a cellular telephone network, or a combinationof two or more of these. A network 110 may include one or more networks110.

Links 150 may connect a client system 130, an assistant system 140, asocial-networking system 160, and a third-party system 170 to acommunication network 110 or to each other. This disclosure contemplatesany suitable links 150. In particular embodiments, one or more links 150include one or more wireline (such as for example Digital SubscriberLine (DSL) or Data Over Cable Service Interface Specification (DOCSIS)),wireless (such as for example Wi-Fi or Worldwide Interoperability forMicrowave Access (WiMAX)), or optical (such as for example SynchronousOptical Network (SONET) or Synchronous Digital Hierarchy (SDH)) links.In particular embodiments, one or more links 150 each include an ad hocnetwork, an intranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN,a MAN, a portion of the Internet, a portion of the PSTN, a cellulartechnology-based network, a satellite communications technology-basednetwork, another link 150, or a combination of two or more such links150. Links 150 need not necessarily be the same throughout a networkenvironment 100. One or more first links 150 may differ in one or morerespects from one or more second links 150.

In particular embodiments, a client system 130 may be an electronicdevice including hardware, software, or embedded logic components or acombination of two or more such components and capable of carrying outthe appropriate functionalities implemented or supported by a clientsystem 130. As an example and not by way of limitation, a client system130 may include a computer system such as a desktop computer, notebookor laptop computer, netbook, a tablet computer, e-book reader, GPSdevice, camera, personal digital assistant (PDA), handheld electronicdevice, cellular telephone, smartphone, smart speaker, virtual reality(VR) headset, augment reality (AR) smart glasses, other suitableelectronic device, or any suitable combination thereof. In particularembodiments, the client system 130 may be a smart assistant device. Moreinformation on smart assistant devices may be found in U.S. patentapplication Ser. No. 15/949,011, filed 9 Apr. 2018, U.S. PatentApplication No. 62/655,751, filed 10 Apr. 2018, U.S. Design patentapplication No. 29/631,910, filed 3 Jan. 2018, U.S. Design patentapplication No. 29/631,747, filed 2 Jan. 2018, U.S. Design patentapplication No. 29/631,913, filed 3 Jan. 2018, and U.S. Design patentapplication No. 29/631,914, filed 3 Jan. 2018, each of which isincorporated by reference. This disclosure contemplates any suitableclient systems 130. A client system 130 may enable a network user at aclient system 130 to access a network 110. A client system 130 mayenable its user to communicate with other users at other client systems130.

In particular embodiments, a client system 130 may include a web browser132 and may have one or more add-ons, plug-ins, or other extensions. Auser at a client system 130 may enter a Uniform Resource Locator (URL)or other address directing a web browser 132 to a particular server(such as server 162, or a server associated with a third-party system170), and the web browser 132 may generate a Hyper Text TransferProtocol (HTTP) request and communicate the HTTP request to server. Theserver may accept the HTTP request and communicate to a client system130 one or more Hyper Text Markup Language (HTML) files responsive tothe HTTP request. The client system 130 may render a web interface (e.g.a webpage) based on the HTML files from the server for presentation tothe user. This disclosure contemplates any suitable source files. As anexample and not by way of limitation, a web interface may be renderedfrom HTML files, Extensible Hyper Text Markup Language (XHTML) files, orExtensible Markup Language (XML) files, according to particular needs.Such interfaces may also execute scripts, combinations of markuplanguage and scripts, and the like. Herein, reference to a web interfaceencompasses one or more corresponding source files (which a browser mayuse to render the web interface) and vice versa, where appropriate.

In particular embodiments, a client system 130 may include asocial-networking application 134 installed on the client system 130. Auser at a client system 130 may use the social-networking application134 to access on online social network. The user at the client system130 may use the social-networking application 134 to communicate withthe user's social connections (e.g., friends, followers, followedaccounts, contacts, etc.). The user at the client system 130 may alsouse the social-networking application 134 to interact with a pluralityof content objects (e.g., posts, news articles, ephemeral content, etc.)on the online social network. As an example and not by way oflimitation, the user may browse trending topics and breaking news usingthe social-networking application 134.

In particular embodiments, a client system 130 may include an assistantapplication 136. A user at a client system 130 may use the assistantapplication 136 to interact with the assistant system 140. In particularembodiments, the assistant application 136 may comprise a stand-aloneapplication. In particular embodiments, the assistant application 136may be integrated into the social-networking application 134 or anothersuitable application (e.g., a messaging application). In particularembodiments, the assistant application 136 may be also integrated intothe client system 130, an assistant hardware device, or any othersuitable hardware devices. In particular embodiments, the assistantapplication 136 may be accessed via the web browser 132. In particularembodiments, the user may provide input via different modalities. As anexample and not by way of limitation, the modalities may include audio,text, image, video, motion, etc. The assistant application 136 maycommunicate the user input to the assistant system 140. Based on theuser input, the assistant system 140 may generate responses. Theassistant system 140 may send the generated responses to the assistantapplication 136. The assistant application 136 may then present theresponses to the user at the client system 130. The presented responsesmay be based on different modalities such as audio, text, image, andvideo. As an example and not by way of limitation, the user may verballyask the assistant application 136 about the traffic information (i.e.,via an audio modality). The assistant application 136 may thencommunicate the request to the assistant system 140. The assistantsystem 140 may accordingly generate the result and send it back to theassistant application 136. The assistant application 136 may furtherpresent the result to the user in text.

In particular embodiments, an assistant system 140 may assist users toretrieve information from different sources. The assistant system 140may also assist user to request services from different serviceproviders. In particular embodiments, the assist system 140 may receivea user request for information or services via the assistant application136 in the client system 130. The assist system 140 may usenatural-language understanding to analyze the user request based onuser's profile and other relevant information. The result of theanalysis may comprise different entities associated with an onlinesocial network. The assistant system 140 may then retrieve informationor request services associated with these entities. In particularembodiments, the assistant system 140 may interact with thesocial-networking system 160 and/or third-party system 170 whenretrieving information or requesting services for the user. Inparticular embodiments, the assistant system 140 may generate apersonalized communication content for the user using natural-languagegenerating techniques. The personalized communication content maycomprise, for example, the retrieved information or the status of therequested services. In particular embodiments, the assistant system 140may enable the user to interact with it regarding the information orservices in a stateful and multi-turn conversation by usingdialog-management techniques. The functionality of the assistant system140 is described in more detail in the discussion of FIG. 2 below.

In particular embodiments, the social-networking system 160 may be anetwork-addressable computing system that can host an online socialnetwork. The social-networking system 160 may generate, store, receive,and send social-networking data, such as, for example, user-profiledata, concept-profile data, social-graph information, or other suitabledata related to the online social network. The social-networking system160 may be accessed by the other components of network environment 100either directly or via a network 110. As an example and not by way oflimitation, a client system 130 may access the social-networking system160 using a web browser 132, or a native application associated with thesocial-networking system 160 (e.g., a mobile social-networkingapplication, a messaging application, another suitable application, orany combination thereof) either directly or via a network 110. Inparticular embodiments, the social-networking system 160 may include oneor more servers 162. Each server 162 may be a unitary server or adistributed server spanning multiple computers or multiple datacenters.Servers 162 may be of various types, such as, for example and withoutlimitation, web server, news server, mail server, message server,advertising server, file server, application server, exchange server,database server, proxy server, another server suitable for performingfunctions or processes described herein, or any combination thereof. Inparticular embodiments, each server 162 may include hardware, software,or embedded logic components or a combination of two or more suchcomponents for carrying out the appropriate functionalities implementedor supported by server 162. In particular embodiments, thesocial-networking system 160 may include one or more data stores 164.Data stores 164 may be used to store various types of information. Inparticular embodiments, the information stored in data stores 164 may beorganized according to specific data structures. In particularembodiments, each data store 164 may be a relational, columnar,correlation, or other suitable database. Although this disclosuredescribes or illustrates particular types of databases, this disclosurecontemplates any suitable types of databases. Particular embodiments mayprovide interfaces that enable a client system 130, a social-networkingsystem 160, or a third-party system 170 to manage, retrieve, modify,add, or delete, the information stored in data store 164.

In particular embodiments, the social-networking system 160 may storeone or more social graphs in one or more data stores 164. In particularembodiments, a social graph may include multiple nodes—which may includemultiple user nodes (each corresponding to a particular user) ormultiple concept nodes (each corresponding to a particular concept)—andmultiple edges connecting the nodes. The social-networking system 160may provide users of the online social network the ability tocommunicate and interact with other users. In particular embodiments,users may join the online social network via the social-networkingsystem 160 and then add connections (e.g., relationships) to a number ofother users of the social-networking system 160 whom they want to beconnected to. Herein, the term “friend” may refer to any other user ofthe social-networking system 160 with whom a user has formed aconnection, association, or relationship via the social-networkingsystem 160.

In particular embodiments, the social-networking system 160 may provideusers with the ability to take actions on various types of items orobjects, supported by the social-networking system 160. As an exampleand not by way of limitation, the items and objects may include groupsor social networks to which users of the social-networking system 160may belong, events or calendar entries in which a user might beinterested, computer-based applications that a user may use,transactions that allow users to buy or sell items via the service,interactions with advertisements that a user may perform, or othersuitable items or objects. A user may interact with anything that iscapable of being represented in the social-networking system 160 or byan external system of a third-party system 170, which is separate fromthe social-networking system 160 and coupled to the social-networkingsystem 160 via a network 110.

In particular embodiments, the social-networking system 160 may becapable of linking a variety of entities. As an example and not by wayof limitation, the social-networking system 160 may enable users tointeract with each other as well as receive content from third-partysystems 170 or other entities, or to allow users to interact with theseentities through an application programming interfaces (API) or othercommunication channels.

In particular embodiments, a third-party system 170 may include one ormore types of servers, one or more data stores, one or more interfaces,including but not limited to APIs, one or more web services, one or morecontent sources, one or more networks, or any other suitable components,e.g., that servers may communicate with. A third-party system 170 may beoperated by a different entity from an entity operating thesocial-networking system 160. In particular embodiments, however, thesocial-networking system 160 and third-party systems 170 may operate inconjunction with each other to provide social-networking services tousers of the social-networking system 160 or third-party systems 170. Inthis sense, the social-networking system 160 may provide a platform, orbackbone, which other systems, such as third-party systems 170, may useto provide social-networking services and functionality to users acrossthe Internet.

In particular embodiments, a third-party system 170 may include athird-party content object provider. A third-party content objectprovider may include one or more sources of content objects, which maybe communicated to a client system 130. As an example and not by way oflimitation, content objects may include information regarding things oractivities of interest to the user, such as, for example, movie showtimes, movie reviews, restaurant reviews, restaurant menus, productinformation and reviews, or other suitable information. As anotherexample and not by way of limitation, content objects may includeincentive content objects, such as coupons, discount tickets, giftcertificates, or other suitable incentive objects.

In particular embodiments, the social-networking system 160 alsoincludes user-generated content objects, which may enhance a user'sinteractions with the social-networking system 160. User-generatedcontent may include anything a user can add, upload, send, or “post” tothe social-networking system 160. As an example and not by way oflimitation, a user communicates posts to the social-networking system160 from a client system 130. Posts may include data such as statusupdates or other textual data, location information, photos, videos,links, music or other similar data or media. Content may also be addedto the social-networking system 160 by a third-party through a“communication channel,” such as a newsfeed or stream.

In particular embodiments, the social-networking system 160 may includea variety of servers, sub-systems, programs, modules, logs, and datastores. In particular embodiments, the social-networking system 160 mayinclude one or more of the following: a web server, action logger,API-request server, relevance-and-ranking engine, content-objectclassifier, notification controller, action log,third-party-content-object-exposure log, inference module,authorization/privacy server, search module, advertisement-targetingmodule, user-interface module, user-profile store, connection store,third-party content store, or location store. The social-networkingsystem 160 may also include suitable components such as networkinterfaces, security mechanisms, load balancers, failover servers,management-and-network-operations consoles, other suitable components,or any suitable combination thereof. In particular embodiments, thesocial-networking system 160 may include one or more user-profile storesfor storing user profiles. A user profile may include, for example,biographic information, demographic information, behavioral information,social information, or other types of descriptive information, such aswork experience, educational history, hobbies or preferences, interests,affinities, or location. Interest information may include interestsrelated to one or more categories. Categories may be general orspecific. As an example and not by way of limitation, if a user “likes”an article about a brand of shoes the category may be the brand, or thegeneral category of “shoes” or “clothing.” A connection store may beused for storing connection information about users. The connectioninformation may indicate users who have similar or common workexperience, group memberships, hobbies, educational history, or are inany way related or share common attributes. The connection informationmay also include user-defined connections between different users andcontent (both internal and external). A web server may be used forlinking the social-networking system 160 to one or more client systems130 or one or more third-party systems 170 via a network 110. The webserver may include a mail server or other messaging functionality forreceiving and routing messages between the social-networking system 160and one or more client systems 130. An API-request server may allow athird-party system 170 to access information from the social-networkingsystem 160 by calling one or more APIs. An action logger may be used toreceive communications from a web server about a user's actions on oroff the social-networking system 160. In conjunction with the actionlog, a third-party-content-object log may be maintained of userexposures to third-party-content objects. A notification controller mayprovide information regarding content objects to a client system 130.Information may be pushed to a client system 130 as notifications, orinformation may be pulled from a client system 130 responsive to arequest received from a client system 130. Authorization servers may beused to enforce one or more privacy settings of the users of thesocial-networking system 160. A privacy setting of a user determines howparticular information associated with a user can be shared. Theauthorization server may allow users to opt in to or opt out of havingtheir actions logged by the social-networking system 160 or shared withother systems (e.g., a third-party system 170), such as, for example, bysetting appropriate privacy settings. Third-party-content-object storesmay be used to store content objects received from third parties, suchas a third-party system 170. Location stores may be used for storinglocation information received from client systems 130 associated withusers. Advertisement-pricing modules may combine social information, thecurrent time, location information, or other suitable information toprovide relevant advertisements, in the form of notifications, to auser.

Assistant Systems

FIG. 2 illustrates an example architecture of the assistant system 140.In particular embodiments, the assistant system 140 may assist a user toobtain information or services. The assistant system 140 may enable theuser to interact with it with multi-modal user input (such as voice,text, image, video, motion) in stateful and multi-turn conversations toget assistance. The assistant system 140 may create and store a userprofile comprising both personal and contextual information associatedwith the user. In particular embodiments, the assistant system 140 mayanalyze the user input using natural-language understanding. Theanalysis may be based on the user profile for more personalized andcontext-aware understanding. The assistant system 140 may resolveentities associated with the user input based on the analysis. Inparticular embodiments, the assistant system 140 may interact withdifferent agents to obtain information or services that are associatedwith the resolved entities. The assistant system 140 may generate aresponse for the user regarding the information or services by usingnatural-language generation. Through the interaction with the user, theassistant system 140 may use dialog management techniques to manage andforward the conversation flow with the user. In particular embodiments,the assistant system 140 may further assist the user to effectively andefficiently digest the obtained information by summarizing theinformation. The assistant system 140 may also assist the user to bemore engaging with an online social network by providing tools that helpthe user interact with the online social network (e.g., creating posts,comments, messages). The assistant system 140 may additionally assistthe user to manage different tasks such as keeping track of events. Inparticular embodiments, the assistant system 140 may proactively executepre-authorized tasks that are relevant to user interests and preferencesbased on the user profile, at a time relevant for the user, without auser input. In particular embodiments, the assistant system 140 maycheck privacy settings to ensure that accessing a user's profile orother user information and executing different tasks are permittedsubject to the user's privacy settings. More information on assistingusers subject to privacy settings may be found in U.S. PatentApplication No. 62/675,090, filed 22 May 2018, which is incorporated byreference.

In particular embodiments, the assistant system 140 may receive a userinput from the assistant application 136 in the client system 130associated with the user. In particular embodiments, the user input maybe a user generated input that is sent to the assistant system 140 in asingle turn. If the user input is based on a text modality, theassistant system 140 may receive it at a messaging platform 205. If theuser input is based on an audio modality (e.g., the user may speak tothe assistant application 136 or send a video including speech to theassistant application 136), the assistant system 140 may process itusing an audio speech recognition (ASR) module 210 to convert the userinput into text. If the user input is based on an image or videomodality, the assistant system 140 may process it using opticalcharacter recognition techniques within the messaging platform 205 toconvert the user input into text. The output of the messaging platform205 or the ASR module 210 may be received at an assistant xbot 215. Moreinformation on handling user input based on different modalities may befound in U.S. patent application Ser. No. 16/053,600, filed 2 Aug. 2018,which is incorporated by reference.

In particular embodiments, the assistant xbot 215 may be a type of chatbot. The assistant xbot 215 may comprise a programmable service channel,which may be a software code, logic, or routine that functions as apersonal assistant to the user. The assistant xbot 215 may work as theuser's portal to the assistant system 140. The assistant xbot 215 maytherefore be considered as a type of conversational agent. In particularembodiments, the assistant xbot 215 may send the textual user input to anatural-language understanding (NLU) module 220 to interpret the userinput. In particular embodiments, the NLU module 220 may get informationfrom a user context engine 225 and a semantic information aggregator(SIA) 230 to accurately understand the user input. The user contextengine 225 may store the user profile of the user. The user profile ofthe user may comprise user-profile data including demographicinformation, social information, and contextual information associatedwith the user. The user-profile data may also include user interests andpreferences on a plurality of topics, aggregated through conversationson news feed, search logs, messaging platform 205, etc. The usage of auser profile may be protected behind a privacy check module 245 toensure that a user's information can be used only for his/her benefit,and not shared with anyone else. More information on user profiles maybe found in U.S. patent application Ser. No. 15/967,239, filed 30 Apr.2018, which is incorporated by reference. The semantic informationaggregator 230 may provide ontology data associated with a plurality ofpredefined domains, intents, and slots to the NLU module 220. Inparticular embodiments, a domain may denote a social context ofinteraction, e.g., education. An intent may be an element in apre-defined taxonomy of semantic intentions, which may indicate apurpose of a user interacting with the assistant system 140. Inparticular embodiments, an intent may be an output of the NLU module 220if the user input comprises a text/speech input. The NLU module 220 mayclassify the text/speech input into a member of the pre-definedtaxonomy, e.g., for the input “Play Beethoven's 5th,” the NLU module 220may classify the input as having the intent [IN:play_music]. Inparticular embodiments, a domain may be conceptually a namespace for aset of intents, e.g., music. A slot may be a named sub-string with theuser input, representing a basic semantic entity. For example, a slotfor “pizza” may be [SL:dish]. In particular embodiments, a set of validor expected named slots may be conditioned on the classified intent. Asan example and not by way of limitation, for [IN:play_music], a slot maybe [SL:song_name]. The semantic information aggregator 230 mayadditionally extract information from a social graph, a knowledge graph,and a concept graph, and retrieve a user's profile from the user contextengine 225. The semantic information aggregator 230 may further processinformation from these different sources by determining what informationto aggregate, annotating n-grams of the user input, ranking the n-gramswith confidence scores based on the aggregated information, formulatingthe ranked n-grams into features that can be used by the NLU module 220for understanding the user input. More information on aggregatingsemantic information may be found in U.S. patent application Ser. No.15/967,342, filed 30 Apr. 2018, which is incorporated by reference.Based on the output of the user context engine 225 and the semanticinformation aggregator 230, the NLU module 220 may identify a domain, anintent, and one or more slots from the user input in a personalized andcontext-aware manner. In particular embodiments, the NLU module 220 maycomprise a lexicon of language and a parser and grammar rules topartition sentences into an internal representation. The NLU module 220may also comprise one or more programs that perform naive semantics orstochastic semantic analysis to the use of pragmatics to understand auser input. In particular embodiments, the parser may be based on a deeplearning architecture comprising multiple long-short term memory (LSTM)networks. As an example and not by way of limitation, the parser may bebased on a recurrent neural network grammar (RNNG) model, which is atype of recurrent and recursive LSTM algorithm. More information onnatural-language understanding may be found in U.S. patent applicationSer. No. 16/011,062, filed 18 Jun. 2018, U.S. patent application Ser.No. 16/025,317, filed 2 Jul. 2018, and U.S. patent application Ser. No.16/038,120, filed 17 Jul. 2018, each of which is incorporated byreference.

In particular embodiments, the identified domain, intent, and one ormore slots from the NLU module 220 may be sent to a dialog engine 235.In particular embodiments, the dialog engine 235 may manage the dialogstate and flow of the conversation between the user and the assistantxbot 215. The dialog engine 235 may additionally store previousconversations between the user and the assistant xbot 215. In particularembodiments, the dialog engine 235 may communicate with an entityresolution module 240 to resolve entities associated with the one ormore slots, which supports the dialog engine 235 to forward the flow ofthe conversation between the user and the assistant xbot 215. Inparticular embodiments, the entity resolution module 240 may access thesocial graph, the knowledge graph, and the concept graph when resolvingthe entities. Entities may include, for example, unique users orconcepts, each of which may have a unique identifier (ID). As an exampleand not by way of limitation, the knowledge graph may comprise aplurality of entities. Each entity may comprise a single recordassociated with one or more attribute values. The particular record maybe associated with a unique entity identifier. Each record may havediverse values for an attribute of the entity. Each attribute value maybe associated with a confidence probability. A confidence probabilityfor an attribute value represents a probability that the value isaccurate for the given attribute. Each attribute value may be alsoassociated with a semantic weight. A semantic weight for an attributevalue may represent how the value semantically appropriate for the givenattribute considering all the available information. For example, theknowledge graph may comprise an entity of a movie “The Martian” (2015),which includes information that has been extracted from multiple contentsources (e.g., movie review sources, media databases, and entertainmentcontent sources), and then deduped, resolved, and fused to generate thesingle unique record for the knowledge graph. The entity may beassociated with a space attribute value which indicates the genre of themovie “The Martian” (2015). More information on the knowledge graph maybe found in U.S. patent application Ser. No. 16/048,049, filed 27 Jul.2018, and U.S. patent application Ser. No. 16/048,101, filed 27 Jul.2018, each of which is incorporated by reference. The entity resolutionmodule 240 may additionally request a user profile of the userassociated with the user input from the user context engine 225. Inparticular embodiments, the entity resolution module 240 may communicatewith a privacy check module 245 to guarantee that the resolving of theentities does not violate privacy policies. In particular embodiments,the privacy check module 245 may use an authorization/privacy server toenforce privacy policies. As an example and not by way of limitation, anentity to be resolved may be another user who specifies in his/herprivacy settings that his/her identity should not be searchable on theonline social network, and thus the entity resolution module 240 may notreturn that user's identifier in response to a request. Based on theinformation obtained from the social graph, knowledge graph, conceptgraph, and user profile, and subject to applicable privacy policies, theentity resolution module 240 may therefore accurately resolve theentities associated with the user input in a personalized andcontext-aware manner. In particular embodiments, each of the resolvedentities may be associated with one or more identifiers hosted by thesocial-networking system 160. As an example and not by way oflimitation, an identifier may comprise a unique user identifier (ID). Inparticular embodiments, each of the resolved entities may be alsoassociated with a confidence score. More information on resolvingentities may be found in U.S. patent application Ser. No. 16/048,049,filed 27 Jul. 2018, and U.S. patent application Ser. No. 16/048,072,filed 27 Jul. 2018, each of which is incorporated by reference.

In particular embodiments, the dialog engine 235 may communicate withdifferent agents based on the identified intent and domain, and theresolved entities. In particular embodiments, an agent may be animplementation that serves as a broker across a plurality of contentproviders for one domain. A content provider may be an entityresponsible for carrying out an action associated with an intent orcompleting a task associated with the intent. As an example and not byway of limitation, multiple device-specific implementations (e.g.,real-time calls for a client system 130 or a messaging application onthe client system 130) may be handled internally by a single agent.Alternatively, these device-specific implementations may be handled bymultiple agents associated with multiple domains. In particularembodiments, the agents may comprise first-party agents 250 andthird-party agents 255. In particular embodiments, first-party agents250 may comprise internal agents that are accessible and controllable bythe assistant system 140 (e.g. agents associated with services providedby the online social network). In particular embodiments, third-partyagents 255 may comprise external agents that the assistant system 140has no control over (e.g., music streams agents, ticket sales agents).The first-party agents 250 may be associated with first-party providers260 that provide content objects and/or services hosted by thesocial-networking system 160. The third-party agents 255 may beassociated with third-party providers 265 that provide content objectsand/or services hosted by the third-party system 170.

In particular embodiments, the communication from the dialog engine 235to the first-party agents 250 may comprise requesting particular contentobjects and/or services provided by the first-party providers 260. As aresult, the first-party agents 250 may retrieve the requested contentobjects from the first-party providers 260 and/or execute tasks thatcommand the first-party providers 260 to perform the requested services.In particular embodiments, the communication from the dialog engine 235to the third-party agents 255 may comprise requesting particular contentobjects and/or services provided by the third-party providers 265. As aresult, the third-party agents 255 may retrieve the requested contentobjects from the third-party providers 265 and/or execute tasks thatcommand the third-party providers 265 to perform the requested services.The third-party agents 255 may access the privacy check module 245 toguarantee no privacy violations before interacting with the third-partyproviders 265. As an example and not by way of limitation, the userassociated with the user input may specify in his/her privacy settingsthat his/her profile information is invisible to any third-party contentproviders. Therefore, when retrieving content objects associated withthe user input from the third-party providers 265, the third-partyagents 255 may complete the retrieval without revealing to thethird-party providers 265 which user is requesting the content objects.

In particular embodiments, each of the first-party agents 250 orthird-party agents 255 may be designated for a particular domain. As anexample and not by way of limitation, the domain may comprise weather,transportation, music, etc. In particular embodiments, the assistantsystem 140 may use a plurality of agents collaboratively to respond to auser input. As an example and not by way of limitation, the user inputmay comprise “direct me to my next meeting.” The assistant system 140may use a calendar agent to retrieve the location of the next meeting.The assistant system 140 may then use a navigation agent to direct theuser to the next meeting.

In particular embodiments, each of the first-party agents 250 orthird-party agents 255 may retrieve a user profile from the user contextengine 225 to execute tasks in a personalized and context-aware manner.As an example and not by way of limitation, a user input may comprise“book me a ride to the airport.” A transportation agent may execute thetask of booking the ride. The transportation agent may retrieve the userprofile of the user from the user context engine 225 before booking theride. For example, the user profile may indicate that the user preferstaxis, so the transportation agent may book a taxi for the user. Asanother example, the contextual information associated with the userprofile may indicate that the user is in a hurry so the transportationagent may book a ride from a ride-sharing service for the user since itmay be faster to get a car from a ride-sharing service than a taxicompany. In particular embodiment, each of the first-party agents 250 orthird-party agents 255 may take into account other factors whenexecuting tasks. As an example and not by way of limitation, otherfactors may comprise price, rating, efficiency, partnerships with theonline social network, etc.

In particular embodiments, the dialog engine 235 may communicate with aconversational understanding composer (CU composer) 270. The dialogengine 235 may send the requested content objects and/or the statuses ofthe requested services to the CU composer 270. In particularembodiments, the dialog engine 235 may send the requested contentobjects and/or the statuses of the requested services as a <k, c, u, d >tuple, in which k indicates a knowledge source, c indicates acommunicative goal, u indicates a user model, and d indicates adiscourse model. In particular embodiments, the CU composer 270 maycomprise a natural-language generator (NLG) 271 and a user interface(UI) payload generator 272. The natural-language generator 271 maygenerate a communication content based on the output of the dialogengine 235. In particular embodiments, the NLG 271 may comprise acontent determination component, a sentence planner, and a surfacerealization component. The content determination component may determinethe communication content based on the knowledge source, communicativegoal, and the user's expectations. As an example and not by way oflimitation, the determining may be based on a description logic. Thedescription logic may comprise, for example, three fundamental notionswhich are individuals (representing objects in the domain), concepts(describing sets of individuals), and roles (representing binaryrelations between individuals or concepts). The description logic may becharacterized by a set of constructors that allow the natural-languagegenerator 271 to build complex concepts/roles from atomic ones. Inparticular embodiments, the content determination component may performthe following tasks to determine the communication content. The firsttask may comprise a translation task, in which the input to thenatural-language generator 271 may be translated to concepts. The secondtask may comprise a selection task, in which relevant concepts may beselected among those resulted from the translation task based on theuser model. The third task may comprise a verification task, in whichthe coherence of the selected concepts may be verified. The fourth taskmay comprise an instantiation task, in which the verified concepts maybe instantiated as an executable file that can be processed by thenatural-language generator 271. The sentence planner may determine theorganization of the communication content to make it humanunderstandable. The surface realization component may determine specificwords to use, the sequence of the sentences, and the style of thecommunication content. The UI payload generator 272 may determine apreferred modality of the communication content to be presented to theuser. In particular embodiments, the CU composer 270 may communicatewith the privacy check module 245 to make sure the generation of thecommunication content follows the privacy policies. In particularembodiments, the CU composer 270 may retrieve a user profile from theuser context engine 225 when generating the communication content anddetermining the modality of the communication content. As a result, thecommunication content may be more natural, personalized, andcontext-aware for the user. As an example and not by way of limitation,the user profile may indicate that the user likes short sentences inconversations so the generated communication content may be based onshort sentences. As another example and not by way of limitation, thecontextual information associated with the user profile may indicatedthat the user is using a device that only outputs audio signals so theUI payload generator 272 may determine the modality of the communicationcontent as audio. More information on natural-language generation may befound in U.S. patent application Ser. No. 15/967,279, filed 30 Apr.2018, and U.S. patent application Ser. No. 15/966,455, filed 30 Apr.2018, each of which is incorporated by reference.

In particular embodiments, the CU composer 270 may send the generatedcommunication content to the assistant xbot 215. In particularembodiments, the assistant xbot 215 may send the communication contentto the messaging platform 205. The messaging platform 205 may furthersend the communication content to the client system 130 via theassistant application 136. In alternative embodiments, the assistantxbot 215 may send the communication content to a text-to-speech (TTS)module 275. The TTS module 275 may convert the communication content toan audio clip. The TTS module 275 may further send the audio clip to theclient system 130 via the assistant application 136.

In particular embodiments, the assistant xbot 215 may interact with aproactive inference layer 280 without receiving a user input. Theproactive inference layer 280 may infer user interests and preferencesbased on the user profile that is retrieved from the user context engine225. In particular embodiments, the proactive inference layer 280 mayfurther communicate with proactive agents 285 regarding the inference.The proactive agents 285 may execute proactive tasks based on theinference. As an example and not by way of limitation, the proactivetasks may comprise sending content objects or providing services to theuser. In particular embodiments, each proactive task may be associatedwith an agenda item. The agenda item may comprise a recurring item suchas a daily digest. The agenda item may also comprise a one-time item. Inparticular embodiments, a proactive agent 285 may retrieve the userprofile from the user context engine 225 when executing the proactivetask. Therefore, the proactive agent 285 may execute the proactive taskin a personalized and context-aware manner. As an example and not by wayof limitation, the proactive inference layer may infer that the userlikes the band Maroon 5 and the proactive agent 285 may generate arecommendation of Maroon 5's new song/album to the user.

In particular embodiments, the proactive agent 285 may generatecandidate entities associated with the proactive task based on a userprofile. The generation may be based on a straightforward backend queryusing deterministic filters to retrieve the candidate entities from astructured data store. The generation may be alternatively based on amachine-learning model that is trained based on the user profile, entityattributes, and relevance between users and entities. As an example andnot by way of limitation, the machine-learning model may be based onsupport vector machines (SVM). As another example and not by way oflimitation, the machine-learning model may be based on a regressionmodel. As another example and not by way of limitation, themachine-learning model may be based on a deep convolutional neuralnetwork (DCNN). In particular embodiments, the proactive agent 285 mayalso rank the generated candidate entities based on the user profile andthe content associated with the candidate entities. The ranking may bebased on the similarities between a user's interests and the candidateentities. As an example and not by way of limitation, the assistantsystem 140 may generate a feature vector representing a user's interestand feature vectors representing the candidate entities. The assistantsystem 140 may then calculate similarity scores (e.g., based on cosinesimilarity) between the feature vector representing the user's interestand the feature vectors representing the candidate entities. The rankingmay be alternatively based on a ranking model that is trained based onuser feedback data.

In particular embodiments, the proactive task may comprise recommendingthe candidate entities to a user. The proactive agent 285 may schedulethe recommendation, thereby associating a recommendation time with therecommended candidate entities. The recommended candidate entities maybe also associated with a priority and an expiration time. In particularembodiments, the recommended candidate entities may be sent to aproactive scheduler. The proactive scheduler may determine an actualtime to send the recommended candidate entities to the user based on thepriority associated with the task and other relevant factors (e.g.,clicks and impressions of the recommended candidate entities). Inparticular embodiments, the proactive scheduler may then send therecommended candidate entities with the determined actual time to anasynchronous tier. The asynchronous tier may temporarily store therecommended candidate entities as a job. In particular embodiments, theasynchronous tier may send the job to the dialog engine 235 at thedetermined actual time for execution. In alternative embodiments, theasynchronous tier may execute the job by sending it to other surfaces(e.g., other notification services associated with the social-networkingsystem 160). In particular embodiments, the dialog engine 235 mayidentify the dialog intent, state, and history associated with the user.Based on the dialog intent, the dialog engine 235 may select somecandidate entities among the recommended candidate entities to send tothe client system 130. In particular embodiments, the dialog state andhistory may indicate if the user is engaged in an ongoing conversationwith the assistant xbot 215. If the user is engaged in an ongoingconversation and the priority of the task of recommendation is low, thedialog engine 235 may communicate with the proactive scheduler toreschedule a time to send the selected candidate entities to the clientsystem 130. If the user is engaged in an ongoing conversation and thepriority of the task of recommendation is high, the dialog engine 235may initiate a new dialog session with the user in which the selectedcandidate entities may be presented. As a result, the interruption ofthe ongoing conversation may be prevented. When it is determined thatsending the selected candidate entities is not interruptive to the user,the dialog engine 235 may send the selected candidate entities to the CUcomposer 270 to generate a personalized and context-aware communicationcontent comprising the selected candidate entities, subject to theuser's privacy settings. In particular embodiments, the CU composer 270may send the communication content to the assistant xbot 215 which maythen send it to the client system 130 via the messaging platform 205 orthe TTS module 275. More information on proactively assisting users maybe found in U.S. patent application Ser. No. 15/967,193, filed 30 Apr.2018, and U.S. patent application Ser. No. 16/036,827, filed 16 Jul.2018, each of which is incorporated by reference.

In particular embodiments, the assistant xbot 215 may communicate with aproactive agent 285 in response to a user input. As an example and notby way of limitation, the user may ask the assistant xbot 215 to set upa reminder. The assistant xbot 215 may request a proactive agent 285 toset up such reminder and the proactive agent 285 may proactively executethe task of reminding the user at a later time.

In particular embodiments, the assistant system 140 may comprise asummarizer 290. The summarizer 290 may provide customized news feedsummaries to a user. In particular embodiments, the summarizer 290 maycomprise a plurality of meta agents. The plurality of meta agents mayuse the first-party agents 250, third-party agents 255, or proactiveagents 285 to generated news feed summaries. In particular embodiments,the summarizer 290 may retrieve user interests and preferences from theproactive inference layer 280. The summarizer 290 may then retrieveentities associated with the user interests and preferences from theentity resolution module 240. The summarizer 290 may further retrieve auser profile from the user context engine 225. Based on the informationfrom the proactive inference layer 280, the entity resolution module240, and the user context engine 225, the summarizer 290 may generatepersonalized and context-aware summaries for the user. In particularembodiments, the summarizer 290 may send the summaries to the CUcomposer 270. The CU composer 270 may process the summaries and send theprocessing results to the assistant xbot 215. The assistant xbot 215 maythen send the processed summaries to the client system 130 via themessaging platform 205 or the TTS module 275. More information onsummarization may be found in U.S. patent application Ser. No.15/967,290, filed 30 Apr. 2018, which is incorporated by reference.

FIG. 3 illustrates an example diagram flow of responding to a userrequest by the assistant system 140. In particular embodiments, theassistant xbot 215 may access a request manager 305 upon receiving theuser request. The request manager 305 may comprise a context extractor306 and a conversational understanding object generator (CU objectgenerator) 307. The context extractor 306 may extract contextualinformation associated with the user request. The context extractor 306may also update contextual information based on the assistantapplication 136 executing on the client system 130. As an example andnot by way of limitation, the update of contextual information maycomprise content items are displayed on the client system 130. Asanother example and not by way of limitation, the update of contextualinformation may comprise alarm is set on the client system 130. Asanother example and not by way of limitation, the update of contextualinformation may comprise a song is playing on the client system 130. TheCU object generator 307 may generate particular content objects relevantto the user request. The content objects may comprise dialog-sessiondata and features associated with the user request, which may be sharedwith all the modules of the assistant system 140. In particularembodiments, the request manager 305 may store the contextualinformation and the generated content objects in data store 310 which isa particular data store implemented in the assistant system 140.

In particular embodiments, the request manger 305 may send the generatedcontent objects to the NLU module 220. The NLU module 220 may perform aplurality of steps to process the content objects. At step 221, the NLUmodule 220 may generate a whitelist for the content objects. Inparticular embodiments, the whitelist may comprise interpretation datamatching the user request. At step 222, the NLU module 220 may perform afeaturization based on the whitelist. At step 223, the NLU module 220may perform domain classification/selection on user request based on thefeatures resulted from the featurization to classify the user requestinto predefined domains. The domain classification/selection results maybe further processed based on two related procedures. At step 224 a, theNLU module 220 may process the domain classification/selection resultusing an intent classifier. The intent classifier may determine theuser's intent associated with the user request. In particularembodiments, there may be one intent classifier for each domain todetermine the most possible intents in a given domain. As an example andnot by way of limitation, the intent classifier may be based on amachine-learning model that may take the domain classification/selectionresult as input and calculate a probability of the input beingassociated with a particular predefined intent. At step 224 b, the NLUmodule may process the domain classification/selection result using ameta-intent classifier. The meta-intent classifier may determinecategories that describe the user's intent. In particular embodiments,intents that are common to multiple domains may be processed by themeta-intent classifier. As an example and not by way of limitation, themeta-intent classifier may be based on a machine-learning model that maytake the domain classification/selection result as input and calculate aprobability of the input being associated with a particular predefinedmeta-intent. At step 225 a, the NLU module 220 may use a slot tagger toannotate one or more slots associated with the user request. Inparticular embodiments, the slot tagger may annotate the one or moreslots for the n-grams of the user request. At step 225 b, the NLU module220 may use a meta slot tagger to annotate one or more slots for theclassification result from the meta-intent classifier. In particularembodiments, the meta slot tagger may tag generic slots such asreferences to items (e.g., the first), the type of slot, the value ofthe slot, etc. As an example and not by way of limitation, a userrequest may comprise “change 500 dollars in my account to Japanese yen.”The intent classifier may take the user request as input and formulateit into a vector. The intent classifier may then calculate probabilitiesof the user request being associated with different predefined intentsbased on a vector comparison between the vector representing the userrequest and the vectors representing different predefined intents. In asimilar manner, the slot tagger may take the user request as input andformulate each word into a vector. The intent classifier may thencalculate probabilities of each word being associated with differentpredefined slots based on a vector comparison between the vectorrepresenting the word and the vectors representing different predefinedslots. The intent of the user may be classified as “changing money”. Theslots of the user request may comprise “500”, “dollars”, “account”, and“Japanese yen”. The meta-intent of the user may be classified as“financial service”. The meta slot may comprise “finance”.

In particular embodiments, the NLU module 220 may improve the domainclassification/selection of the content objects by extracting semanticinformation from the semantic information aggregator 230. In particularembodiments, the semantic information aggregator 230 may aggregatesemantic information in the following way. The semantic informationaggregator 230 may first retrieve information from the user contextengine 225. In particular embodiments, the user context engine 225 maycomprise offline aggregators 226 and an online inference service 227.The offline aggregators 226 may process a plurality of data associatedwith the user that are collected from a prior time window. As an exampleand not by way of limitation, the data may include news feedposts/comments, interactions with news feed posts/comments, searchhistory, etc. that are collected from a prior 90-day window. Theprocessing result may be stored in the user context engine 225 as partof the user profile. The online inference service 227 may analyze theconversational data associated with the user that are received by theassistant system 140 at a current time. The analysis result may bestored in the user context engine 225 also as part of the user profile.In particular embodiments, both the offline aggregators 226 and onlineinference service 227 may extract personalization features from theplurality of data. The extracted personalization features may be used byother modules of the assistant system 140 to better understand userinput. In particular embodiments, the semantic information aggregator230 may then process the retrieved information, i.e., a user profile,from the user context engine 225 in the following steps. At step 231,the semantic information aggregator 230 may process the retrievedinformation from the user context engine 225 based on natural-languageprocessing (NLP). In particular embodiments, the semantic informationaggregator 230 may tokenize text by text normalization, extract syntaxfeatures from text, and extract semantic features from text based onNLP. The semantic information aggregator 230 may additionally extractfeatures from contextual information, which is accessed from dialoghistory between a user and the assistant system 140. The semanticinformation aggregator 230 may further conduct global word embedding,domain-specific embedding, and/or dynamic embedding based on thecontextual information. At step 232, the processing result may beannotated with entities by an entity tagger. Based on the annotations,the semantic information aggregator 230 may generate dictionaries forthe retrieved information at step 233. In particular embodiments, thedictionaries may comprise global dictionary features which can beupdated dynamically offline. At step 234, the semantic informationaggregator 230 may rank the entities tagged by the entity tagger. Inparticular embodiments, the semantic information aggregator 230 maycommunicate with different graphs 330 including social graph, knowledgegraph, and concept graph to extract ontology data that is relevant tothe retrieved information from the user context engine 225. Inparticular embodiments, the semantic information aggregator 230 mayaggregate the user profile, the ranked entities, and the informationfrom the graphs 330. The semantic information aggregator 230 may thensend the aggregated information to the NLU module 220 to facilitate thedomain classification/selection.

In particular embodiments, the output of the NLU module 220 may be sentto a co-reference module 315 to interpret references of the contentobjects associated with the user request. In particular embodiments, theco-reference module 315 may be used to identify an item to which theuser request refers. The co-reference module 315 may comprise referencecreation 316 and reference resolution 317. In particular embodiments,the reference creation 316 may create references for entities determinedby the NLU module 220. The reference resolution 317 may resolve thesereferences accurately. As an example and not by way of limitation, auser request may comprise “find me the nearest gas station and direct methere”. The co-reference module 315 may interpret “there” as “thenearest gas station”. In particular embodiments, the co-reference module315 may access the user context engine 225 and the dialog engine 235when necessary to interpret references with improved accuracy.

In particular embodiments, the identified domains, intents,meta-intents, slots, and meta slots, along with the resolved referencesmay be sent to the entity resolution module 240 to resolve relevantentities. The entity resolution module 240 may execute generic anddomain-specific entity resolution. In particular embodiments, the entityresolution module 240 may comprise domain entity resolution 241 andgeneric entity resolution 242. The domain entity resolution 241 mayresolve the entities by categorizing the slots and meta slots intodifferent domains. In particular embodiments, entities may be resolvedbased on the ontology data extracted from the graphs 330. The ontologydata may comprise the structural relationship between differentslots/meta-slots and domains. The ontology may also comprise informationof how the slots/meta-slots may be grouped, related within a hierarchywhere the higher level comprises the domain, and subdivided according tosimilarities and differences. The generic entity resolution 242 mayresolve the entities by categorizing the slots and meta slots intodifferent generic topics. In particular embodiments, the resolving maybe also based on the ontology data extracted from the graphs 330. Theontology data may comprise the structural relationship between differentslots/meta-slots and generic topics. The ontology may also compriseinformation of how the slots/meta-slots may be grouped, related within ahierarchy where the higher level comprises the topic, and subdividedaccording to similarities and differences. As an example and not by wayof limitation, in response to the input of an inquiry of the advantagesof a brand of electric car, the generic entity resolution 242 mayresolve the brand of electric car as vehicle and the domain entityresolution 241 may resolve the brand of electric car as electric car.

In particular embodiments, the output of the entity resolution module240 may be sent to the dialog engine 235 to forward the flow of theconversation with the user. The dialog engine 235 may comprise dialogintent resolution 236 and dialog state update/ranker 237. In particularembodiments, the dialog intent resolution 236 may resolve the userintent associated with the current dialog session based on dialoghistory between the user and the assistant system 140. The dialog intentresolution 236 may map intents determined by the NLU module 220 todifferent dialog intents. The dialog intent resolution 236 may furtherrank dialog intents based on signals from the NLU module 220, the entityresolution module 240, and dialog history between the user and theassistant system 140. In particular embodiments, the dialog stateupdate/ranker 237 may update/rank the dialog state of the current dialogsession. As an example and not by way of limitation, the dialog stateupdate/ranker 237 may update the dialog state as “completed” if thedialog session is over. As another example and not by way of limitation,the dialog state update/ranker 237 may rank the dialog state based on apriority associated with it.

In particular embodiments, the dialog engine 235 may communicate with atask completion module 335 about the dialog intent and associatedcontent objects. In particular embodiments, the task completion module335 may rank different dialog hypotheses for different dialog intents.The task completion module 335 may comprise an action selectioncomponent 336. In particular embodiments, the dialog engine 235 mayadditionally check against dialog policies 320 regarding the dialogstate. In particular embodiments, a dialog policy 320 may comprise adata structure that describes an execution plan of an action by an agent340. An agent 340 may select among registered content providers tocomplete the action. The data structure may be constructed by the dialogengine 235 based on an intent and one or more slots associated with theintent. A dialog policy 320 may further comprise multiple goals relatedto each other through logical operators. In particular embodiments, agoal may be an outcome of a portion of the dialog policy and it may beconstructed by the dialog engine 235. A goal may be represented by anidentifier (e.g., string) with one or more named arguments, whichparameterize the goal. As an example and not by way of limitation, agoal with its associated goal argument may be represented as{confirm_artist, args: {artist: “Madonna”}}. In particular embodiments,a dialog policy may be based on a tree-structured representation, inwhich goals are mapped to leaves of the tree. In particular embodiments,the dialog engine 235 may execute a dialog policy 320 to determine thenext action to carry out. The dialog policies 320 may comprise genericpolicy 321 and domain specific policies 322, both of which may guide howto select the next system action based on the dialog state. Inparticular embodiments, the task completion module 335 may communicatewith dialog policies 320 to obtain the guidance of the next systemaction. In particular embodiments, the action selection component 336may therefore select an action based on the dialog intent, theassociated content objects, and the guidance from dialog policies 320.

In particular embodiments, the output of the task completion module 335may be sent to the CU composer 270. In alternative embodiments, theselected action may require one or more agents 340 to be involved. As aresult, the task completion module 335 may inform the agents 340 aboutthe selected action. Meanwhile, the dialog engine 235 may receive aninstruction to update the dialog state. As an example and not by way oflimitation, the update may comprise awaiting agents' response. Inparticular embodiments, the CU composer 270 may generate a communicationcontent for the user using the NLG 271 based on the output of the taskcompletion module 335. In particular embodiments, the NLG 271 may usedifferent language models and/or language templates to generate naturallanguage outputs. The generation of natural language outputs may beapplication specific. The generation of natural language outputs may bealso personalized for each user. The CU composer 270 may also determinea modality of the generated communication content using the UI payloadgenerator 272. Since the generated communication content may beconsidered as a response to the user request, the CU composer 270 mayadditionally rank the generated communication content using a responseranker 273. As an example and not by way of limitation, the ranking mayindicate the priority of the response.

In particular embodiments, the output of the CU composer 270 may be sentto a response manager 325. The response manager 325 may performdifferent tasks including storing/updating the dialog state 326retrieved from data store 310 and generating responses 327. Inparticular embodiments, the output of CU composer 270 may comprise oneor more of natural-language strings, speech, actions with parameters, orrendered images or videos that can be displayed in a VR headset or ARsmart glass. As a result, the response manager 325 may determine whattasks to perform based on the output of CU composer 270. In particularembodiments, the generated response and the communication content may besent to the assistant xbot 215. In alternative embodiments, the outputof the CU composer 270 may be additionally sent to the TTS module 275 ifthe determined modality of the communication content is audio. Thespeech generated by the TTS module 275 and the response generated by theresponse manager 325 may be then sent to the assistant xbot 215.

Assistant Summarization for Assistant Systems

In particular embodiments, the assistant system 140 may summarizecontent to present to a user. Users may periodically pause in theirconsumption of media whether it be emails, group chats, interacting withhis or her online social network, and the like. The pause may be theresult of a user taking a vacation (e.g., pause in work emails), takinga break from social media (e.g., pause in social networking activity),and the like. The pause in checking content, such as user communications(e.g., electronic communications, messages, and the like), may create abacklog of content that the user may want to address or view at a laterdate. To avoid the time-consuming task of reading through every singlecontent item, the assistant system 140 may provide a summarization ofcontent (e.g., user communications, such as text messages, emails, postson an online social network, etc.) the user has missed. As an exampleand not by way of limitation, the user may ask, “Hey Assistant,summarize my unread work emails,” and the assistant system 140 maygenerate a summary of the work emails that the user has missed. Togenerate the summary of work emails, the assistant system may identifyseparate email threads to summarize and provide the separate summariesof each segment to the user. In particular embodiments, in order toinvoke the summarization, the user may perform an action to request theassistant system 140 to generate a summary of a specific content source(e.g., within an electronic communications inbox, a message thread, anewsfeed, etc.) the user is in. As an example and not by way oflimitation, the user may input a request into a composer interface for asummary of missed activity. The assistant system 140 may use a userprofile of the requesting user to identify content the user is mostinterested in and prioritize that content to summarize for the user toview. After the assistant system 140 compiles the content, the assistantsystem 140 may send the summarized content for the user to view. Thesummarized content may be presented in any suitable modality (e.g.,text/images, or audio/video on a client system 130). Although thisdisclosure describes summarizing content by an assistant system 140 topresent to a user in a particular manner, this disclosure contemplatessummarizing content by an assistant system 140 to present to a user inany suitable manner.

In particular embodiments, the assistant system 140 may receive arequest for a summary of user communications from a content source. Inparticular embodiments, the assistant system 140 may receive the requestfrom a client system 130 associated with the user. As an example and notby way of limitation, the user may input “Hey Assistant, what did I missin my work emails?” into a client system 130, where the input may be inany suitable modality (e.g., text, audio, touch/motion, etc.). Inparticular embodiments, the request for the summary may be one or moreof receiving a text input, an audio input, a visual input, or aselection of an activatable element. As an example and not by way oflimitation, the user may input a text input of the above input, the usermay say the above input, the user may perform a gesture that isassociated with a summarization feature, or any combination. As anotherexample and not by way of limitation, the user may be presented with aninterface through a client system 130 and select an activatable element,such as a button for summarizing content. The user may be presented withan interface on a display of a client system 130, such as a messaginginterface to communicate with the assistant system 140 or a messaginginterface with integrated assistant system 140 features. Additionally,the user may be presented with an interface as an overlay within anaugmented reality interface or an interface within a virtual realityspace through client system 130. In particular embodiments, the requestfor a summary of user communications may be a general request, which maygenerate summaries from a plurality of content sources. In particularembodiments, the content sources may comprise one or more of anelectronic communications inbox, a message thread, a newsfeed, anothersuitable content source, or any combination thereof. As an example andnot by way of limitation, the user may ask “Assistant, summarize thecontent I missed the past week.” The assistant system 140 may summarizean electronic communications inbox and several message threads topresent the summary to the user. In particular embodiments, the contentto summarize may be user communications that are authored by a pluralityof users. As an example and not by way of limitation, the usercommunications may include emails sent by different users, messageswithin a group message, and the like. Although this disclosure describesreceiving a request for a summary of user communications in a particularmanner, this disclosure contemplates receiving a request for a summaryof user communications in any suitable manner.

In particular embodiments, the assistant system 140 may access contentfrom a content source. In particular embodiments, the assistant system140 may access user communications from a content source. The contentmay be accessed subject to authorization/privacy settings associatedwith the user or the content source. As an example and not by way oflimitation, if the user is requesting a summary of work emails, theassistant system 140 may access the electronic communications inboxassociated with the user's work emails and pull all of the emails tosummarize for the user. In particular embodiments, the assistant system140 may determine a time period associated with the request for thesummary of user communications. In particular embodiments, the requestfor a summary may comprise a time period to summarize content. As anexample and not by way of limitation, the user may ask “Assistant,summarize my emails from the past week.” As another example and not byway of limitation, the user may ask “Assistant, summarize my emails fromwhen I was away” and the assistant system 140 may determine an initialstart to when the user was away to determine the time period. This timeperiod may be used to access a set of user communications that aregenerated within the time period. In particular embodiments, theassistant system 140 may determine the time period that corresponds tothe last user communication read or sent by the user. As an example andnot by way of limitation, if the user has been on vacation and not readany work emails, the assistant system 140 may determine the request forsummary of user communications may be for all emails that the user hasmissed while the user has been away. As another example and not by wayof limitation, for group messages, the assistant system 140 maydetermine the last message read by the user and prepare a summarizationof user communications with the next message after the last messageread. As another example and not by way of limitation, the assistantsystem 140 may determine when the user last sent a message to determinewhich user communications to start the summarization process. This maybe for the instance where a user may still read work emails while onvacation but does not respond. In particular embodiments, the assistantsystem 140 may a user profile of the user to identify which usercommunications to access. As an example and not by way of limitation, ifthe user receives daily emails from a specific company email address butrarely reads them, the assistant system 140 may ignore those emails whenaccessing user communications. Although this disclosure describesaccessing particular content from particular content sources in aparticular manner, this disclosure contemplates accessing any suitablecontent from any suitable content sources in any suitable manner.

In particular embodiments, the assistant system 140 may identifysegments associated with the plurality of user communications. Thesegments may be identified by identifying different threads,conversations, or the like within accessed user communication. As anexample and not by way of limitation, for accessed work emails from anelectronic communication inbox, the assistant system 140 may identifythe separate email threads as separate segments to summarize. Generally,each separate email thread may be a different conversation or chain ofcommunications that may be summarized individually. However, if thetopics within the email thread change, the email thread may further besegmented to identify the separate topics discussed to be summarized. Asanother example and not by way of limitation, the assistant system 140may identify separate or different conversations within a group message.The topics within a group message may change periodically as theconversation continues. The assistant system 140 may identify the changein topics to summarize the user communication the user may be interestin. In particular embodiments, each identified segment may be associatedwith a topic of a plurality of topics. The assistant system 140 maycreate segments of user communications based on the associated topics.In particular embodiments, the assistant system 140 may perform a timeanalysis on the accessed user communication to identify segments. Inparticular embodiments, the assistant system 140 may identify twoconsecutive user communications that were generated over a time periodthe exceeds a predetermined time period. As an example and not by way oflimitation, the assistant system 140 may see two messages were sent fivedays apart and identify those messages to be a part of two separatesegments (e.g., the two messages probably don't relate to one another).In particular embodiments, the assistant system 140 may perform a topicanalysis on the accessed user communication to identify segments. Inparticular embodiments, the assistant system 140 may analyzing the usercommunications (e.g., parse the user communications) to identify a topicassociated with the user communication. In particular embodiments, theassistant system 140 may identify two separate segments based on changeof topics between two consecutive user communications. Each segment maycomprise user communications of a same topic. In particular embodiments,multiple segments may be grouped together to be processed to generate asummary. As an example and not by way of limitation, if a group messagecontained messages pertaining to an upcoming hiking event to see whowithin the group would be interested in going and then the topicswitches to relationships and switches again back to confirm who all cango to the hiking event, the assistant system 140 may group the twoseparate segments that relate to the hiking event together to generate asummary. Although this disclosure describes identifying segmentsassociated with the plurality of user communications in a particularmanner, this disclosure contemplates identifying segments associatedwith the plurality of user communications in any suitable manner.

In particular embodiments, the assistant system 140 may calculate a userinterest score for each identified segment. The user interest score mayrepresent, for example, a level of interest, a confidence score, or apredicted probability that the user is interested in the contentassociated with the segment or that the user will interact with suchcontent. In particular embodiments, the assistant system 140 may compilea user profile of the user that comprises information indicative of theuser's interests and user behavior. As an example and not by way oflimitation, the user profile may provide information that the user isinterested in outdoor activities based on her likes of hiking pages androck climbing pages. Based on the information that the user likesoutdoor activities, if an outdoor activity is discussed within a groupmessage, the assistant system 140 may prioritize generating a summary ofthat discussion for the user because the user may be inclined to readthe summary. As another example and not by way of limitation, the userprofile may capture user behavior, such as which other users the usertypically responds to. If the user usually is engaged in discussionwithin a group message with a particular user, the assistant system 140may prioritize summarizing the discussion including that particularuser. In particular embodiments, the assistant system 140 may assignvalues to each identified segment within the accessed usercommunications based on the user interest and user behavior to give moreweight to segments the user may be interested in. In particularembodiments, the calculation of user interest scores may be based onpriority, such as whether a deadline is coming up. As an example and notby way of limitation, if the segment is an email thread of an upcomingproject, the assistant system 140 may calculate a higher user interestscore for the segment because the user may want to see a summarizationof the segment. In particular embodiments, the assistant system 140 maygive different weights to each factor to calculate a user interest scorefor a segment. As an example and not by way of limitation, if a groupmessage has discussion of two events to summarize, a certain weight maybe assigned to the topic of each event and the deadline associated witheach event. If there is a networking event that is coming up thisweekend, the assistant system 140 may assign a user interest score of 70to the networking event based on the priority of the event but theinfrequent interest of the user in networking events. Additionally, ifthere is a road trip that is planned for later on in the month, theassistant system 140 may assign a user interest score of 90 to the roadtrip event based on the lack of priority but high interest of the userin road trips. The assistant system 140 may look at these differentfactors to determine which segments, which each correspond to differenttopics, should be assigned higher user interest scores. In particularembodiments, multiple segments may have similar or the same userinterest score to indicate they have the similar or same significance tothe user. Although this disclosure describes calculating user interestscores for particular segments in a particular manner, this disclosurecontemplates determining user interest scores for any suitable segmentsin any suitable manner.

In particular embodiments, the assistant system 140 may select segmentsfor summarization based on their user interest scores. After calculatingthe user interest scores for each segment as described above, inparticular embodiments, the assistant system 140 may select a thresholdnumber of segments to summarize. As an example and not by way oflimitation, the assistant system 140 may select the top ten segments (ifthere are that many different segments) based on their respective userinterest scores (e.g., top 10 highest user interest scores) to presentthe user an amount of summaries that may not overwhelm the user. Inparticular embodiments, the assistant system 140 may select each segmentthat has a user interest score that exceeds a threshold user interestscore. As an example and not by way of limitation, the assistant system140 may select segments to generate summaries for based on the userinterest score. If there are twenty segments with a user interest score90, the assistant system 140 may generate twenty summaries (e.g., ifeach of those segments are associated with a project with a deadline).In particular embodiments, the assistant system 140 may factor in theuser interest score and a threshold number of segments to select forsummarization. As an example and not by way of limitation, if theassistant system 140 has only identified two segments to summarize, buttheir user interest score is relatively low, the assistant system 140may proceed to select the two segments for summarization. As anotherexample and not by way of limitation, if there are a large number (e.g.,twenty) of segments with high user interest scores, that assistantsystem 140 may adjust a threshold user interest score each segment mustexceed to be selected. This may lead to exclusion of segments that donot have an immediate deadline or the like and present the user withsummaries of segments without overwhelming the user. Although thisdisclosure describes selecting segments for summarization in aparticular manner, this disclosure contemplates selecting segments forsummarization in any suitable manner.

In particular embodiments, the assistant system 140 may generatepersonalized summaries for each selected segment. After the assistantsystem 140 selects the segments to generate summaries for, in particularembodiments, the assistant system 140 may parse through the usercommunications within the selected segment to determine what to includewithin the summarization of the discussion of the selected segment. Asan example and not by way of limitation, if the user communicationswithin a selected segment discusses a road trip on a certain date andpeople confirming to go, the assistant system 140 may summarize thediscussion with brief details on the trip and who all is going and anyitems requested to attend the trip. In particular embodiments, theassistant system 140 may generate semantic embeddings of each usercommunication within a selected segment. In particular embodiments, theassistant system 140 may use a machine-learning model where the semanticembeddings of each user communication within the selected segment isused as an input to generate a personalized summary. In particularembodiments, the assistant system 140 may use a user profile to generatea personalized summary. As an example and not by way of limitation, theassistant system 140 may generate a summary to include things the usermay be more interested in within the discussion of a selected segment.So while two users may request a summary of a selected segment that mayinclude the same user communications, each user may receive a differentsummary based on their different interests and other factors based ontheir user profile. As another example and not by way of limitation, theassistant system 140 may access the user profile of the user todetermine user behavior and generate a summary based on the userbehavior. This may result in a summary that includes what is discussedby a particular user over another user based on the user behavior thatthe user usually engages in conversation with the particular user andnot the other user. Although this disclosure describes generatingpersonalized summaries in a particular manner, this disclosurecontemplates generating personalized summaries in any suitable manner.

In particular embodiments, the assistant system 140 may sendinstructions to a client system 130 associated with the user to presentthe personalized summaries. In particular embodiments, after theassistant system 140 has generated the personalized summaries of theselected segments, the assistant system 140 may send instructions to theclient system 130 to present the personalized summaries. In particularembodiments, the assistant system 140 may present the summaries in aplurality of different modalities. In particular embodiments, theinstructions to present the personalized summaries may includeinstructions to present the personalized summaries as a text output, anaudio output, or a visual output. As an example and not by way oflimitation, the client system 130 may present the personalized summariesas a text block in response to the user's request of a summary of theuser communications. As another example and not by way of limitation,the client system 130 may present the personalized summaries as asummary interface within a virtual reality environment in response tothe request for a summary of a content source. In particularembodiments, the assistant system 140 may rank the personalizedsummaries based on their user interest scores associated with thesegments that correspond with the personalized summary. In particularembodiments, the assistant system 140 may present the personalizedsummaries based on their user interest scores. As an example and not byway of limitation, if a summary of a road trip has a user interest scoreof 85 and a summary of a work function has a user interest score of 60,the assistant system 140 may present the summary of the road trip firstbecause the user may be more interested in the road trip. In particularembodiments, the assistant system 140 may update a user profile based onthe user behavior of the user with respect to the previously generatedpersonalized summaries. As an example and not by way of limitation, if apersonalized summary was generated and the user had to go back to a partof the conversation to read more into the discussion. The assistantsystem 140 may make adjustments to generate a summary that the user willnot need to go back within the content source to review. In particularembodiments, the assistant system 140 may send an interactive elementwithin each summary that may be selectable to respond to the discussionassociated with the summary. As an example and not by way of limitation,the summary may have a button for the user to click to be presented withan email thread to respond to. The click of the button may result in areply email being generated. As another example and not by way oflimitation, the interactive element may be a response to a generatedevent, such as “Yes, I'm going” or “No, I'm not going.” In particularembodiments, if the assistant system 140 generated summaries formultiple content sources, the assistant system 140 may organize thedifferent summaries in any order suitable to present to the user. As anexample and not by way of limitation, the assistant system 140 maypresent the summaries in order of the different content sources or mixthe summaries together and rank them based on user interest scores topresent to the user. Although this disclosure describes presenting thepersonalized summaries in a particular manner, this disclosurecontemplates presenting the personalized summaries in any suitablemanner.

FIG. 4 illustrates an example diagram flow of generating summaries by anassistant system 140 for a content source. The process begins with theclient system 130 receiving a user input 405 from a user. In particularembodiments, the messaging platform 205 may receive the user input 405from the client system 130 via a messaging interface. The user input 405may comprise one or more of a character string, an audio clip, an image,or a video clip. In particular embodiments, the assistant xbot 215 mayreceive the user input 405 from the messaging platform 205. Theassistant xbot 215 may generate a textual input 410 from the user input405. In particular embodiments, the assistant xbot 215 may send thetextual input 410 to the NLU module 220. In particular embodiments, theASR module 210 may receive the user input 405 from the client system 130and generate a textual input 410 to send to the NLU 220. The NLU module220 may identify one or more intents and one or more slots 415 based onthe textual input 410 of the user input 405. After identifying theintents and slots 415, the NLU module 220 may send the intents and slots415 to the dialog engine 235. In particular embodiments, the dialogengine 235 may send the intents and slots 415 to the content agent 420.In particular embodiments, the content agent 420 may comprise one ormore of the first-party agents 250 and the third-party agents 255. Inparticular embodiments, the content agent 420 may identify a queryassociated with the intents and slots 415 and generate search resultscorresponding to the query. In particular embodiments, the content agent420 may send a response 425 comprising the results to the dialog engine235. In particular embodiments, the content agent 420 may identify arequest for a summary from the received intents and slots 415. Thecontent agent 420 may send the response 425 to the dialog engine 235including a request for a summary of a particular content source.

In particular embodiments, the dialog engine 235 may send a request 430to the summarization agent 435 for a summary of a content source 445.The request 430 may comprise an identifier of a content source 445 togenerate a summary. In particular embodiments, the request 430 maycomprise a time period associated with the request 430. In particularembodiments, the dialog engine 235 may determine a request for a summaryof a content source 445 from the received intents and slots 415. Afterreceiving the request 430, in particular embodiments, the summarizationagent 435 may identify a content source 445 associated with the request430 and send a request 440 to the content source 445 to access usercommunications or content. The content may be accessed subject toauthorization/privacy settings associated with the user or the contentsource. In particular embodiments, the summarization agent 435 maydetermine a time period associated with the request 430 for a summary.In particular embodiments, the content source 445 may send back aresponse 450 to the summarization agent 435 comprising the requesteduser communications. In particular embodiments, the summarization agent435 may perform a segmentation process to identify and select segmentsto generate a summary. The summarization agent 435 may generate asummary of the selected segments of user communications and send thesummaries 455 to the dialog engine 235. In particular embodiments, thedialog engine 235 may receive a request for user communications orcontent from a content source 445 from either the summarization agent435 during the summarization process or from the content agent 420 whensending the request for a summary through response 425. The dialogengine 235 may send a request 460 to content source 445 to access usercommunications to generate a summary. The content source 445 may returnthe requested user communications through response 465. After the dialogengine 235 receives the requested user communications, in particularembodiments, the dialog engine 235 may send the request 430 with theuser communications to the summarization agent 435 to generate a summaryof the received user communications.

In particular embodiments, after the dialog engine 235 receives thesummaries 455 from the summarization agent 435, the dialog engine 2356may send a request 470 to the CU composer 270 to modify the summaries455 to include language the user is more familiar with. In particularembodiments, the CU composer 270 may send the summaries 475 to theassistant xbot 215 to pass to the messaging platform 205 andsubsequently send to the client system 130 to present to the user asdescribed above.

FIGS. 5A-5C illustrates example user interfaces and back end processduring the generation of summaries for a content source. Referring toFIG. 5A, a client system 130 may display a messaging interface 502 wherea user may be interacting with a messaging application that includesfunctionality of an assistant system 140. The messaging interface 502may comprise a plurality of user communications 504 a-504 d fromdifferent users. In particular embodiments, the messaging interface 502may include a plurality of buttons and a composer interface. Inparticular embodiments, the messaging interface 502 may comprise aninteractive element 506 that may be selectable to initiate asummarization process 510 of the user communications 504 a-504 d. Asshown in FIG. 5A, the user may select the interactive element 506 with atouch element 508 to request a summary of the user communications 504a-504 d within the messaging interface 502. In particular embodiments,the touch element 506 may be a finger of the user. In particularembodiments, the selection of the interactive element 506 providespermission to the assistant system 140 to access the user communications504 a-504 d. Although shown within a messaging interface 502, inparticular embodiments, the user may vocally request or perform agesture to request a summary of a content source. The general requestmay generate summaries of multiple messaging threads to present to theuser. In particular embodiments, the user may include an identifier torequest a particular summary of a content source. As an example and notby way of limitation, the user may verbally request “Assistant, provideme a summary of unread messages” or the user may verbally request“Assistant, provide me a summary of unread messages of the group withTom.”

FIG. 5B illustrates the result of selecting the interactive element 506,which sends user communications 504 a-504 d to the assistant system 140through the messaging interface 502. In particular embodiments, thesummarization process 510 may include segmenting the user communications504 a-504 d into different time blocks 512 a-512 b based on the when theuser communications 504 a-504 d were received. In particularembodiments, the time blocks 512 a-512 b may be generated forconsecutive user communications that are generated over a threshold timeperiod (e.g., 1 day). In particular embodiments, the assistant system140 may identify segments associated with the user communications 504a-504 d. In particular embodiments, the assistant system 140 mayidentify topics 514 a-514 b associated with each user communication 504a-504 d that may be separate from the time blocks 512 a-512 d. Inparticular embodiments, the assistant system 140 may generate summaries520 a-520 b of the identified segments based on the time blocks 512a-512 b or the topics 514 a-514 b. In particular embodiments, theassistant system 140 may generate semantic embeddings for each of theuser communications 504 a-504 d to be used to generate a summaries 520a-520 b corresponding to the segments associated with the usercommunications 504 a-504 d.

FIG. 5C illustrates the result after generating the summaries 520 a-520b for the user communications 504 a-504 d. In particular embodiments,the assistant system 140 may present a messaging interface 516 that maybe separate from the messaging interface 502. In particular embodiments,the messaging interface 516 may be a messaging interface with anassistant system 140. In particular embodiments, the messaging interface516 may comprise an introductory message 518 the reference the summaries520 a-520 b. In particular embodiments, the summaries 520 a-520 b maycomprise essential brief details associated with the user communications504 a-504 d associated with them, respectively. In particularembodiments, each summary 520 a, 520 b may comprise an interactiveelement 522 a, 522 b. In particular embodiments, the interactive element522 a, 522 b may be selectable to perform a context switch back to themessaging interface 502 that comprises the user communication 504 thatcorresponds with the summary 520. In particular embodiments, theassistant system 140 may send an audio output to present the summaries520 a-520 b to the user. In particular embodiments, the assistant system140 may assign scores to each of the summaries 520 a-520 b and rearrangethe order to present the summaries 520 a-520 b in an order the userwould be most interested in viewing the summary.

FIGS. 6A-6B illustrate an example process of segmenting usercommunications. Referring to FIG. 6A, a group conversation 602 is shownwith a plurality of user communications 604. In particular embodiments,the group conversation 602 may be identified as content to be summarizedfor the user. In particular embodiments, the assistant system 140 mayreceive the group conversation 602 from a client system 130. Inparticular embodiments, the assistant system 140 may receive groupconversation 602 from a content source. The content may be accessedsubject to authorization/privacy settings associated with the user orthe content source. In particular embodiments, the assistant system 140may segment the group conversation 602 to generate a summary of theidentified segments.

FIG. 6B illustrates a segmentation process 605 of the usercommunications 604. In particular embodiments, the assistant system 140may identify separate time blocks 606 a-606 c associated with the usercommunications 604 based on when the user communications 604 aregenerated. User communications 604 a-604 c may be associated with thetime block 606 a. In particular embodiments, the assistant system 140may analyze the user communications 604 to identify a topic 608associated with the user communications. As an example and not by way oflimitation, as “Dancing” was discussed as a potential event, theassistant system 140 identified the topic associated with the usercommunications 604 a-604 c is an event labeled “Dancing.” In particularembodiments, the assistant system 140 may generate a semantic embeddingfor each of the user communications 604. The assistant system 140 mayuse the semantic embeddings of each user communication 604 to generate asummary associated with the segment comprising the respective usercommunication 604. In particular embodiments, the same topic 608 may beidentified in two separate time blocks 606. As an example and not by wayof limitation, the assistant system 140 may identify the usercommunications 604 are discussing a wedding topic 608 b in time block606 b and also in time block 606 c. In particular embodiments, theassistant system 140 may combine several segments together to generate asummary of the combined segment. As shown in FIG. 6B, the usercommunications 604 associated with the dancing event topic 608 a arecombined into a single segment comprising all user communications 604associated with the topic 608 a. Additionally, the user communications604 associated with the wedding query topic 608 b are combined into asingle segment. In particular embodiments, the assistant system 140 maygenerate summaries based on the combined segment to accurately summarizethe discussion. The benefit of this allows for any changes laterdiscussed to be captured by the summarization of the whole entire topic(e.g., if a user changes his or her mind).

FIG. 7 illustrates an example method 700 for generating summaries by anassistant system 140 for a content source. The method may begin at step710, where the assistant system 140 may receive a request for a summaryof user communications from a first content source from a client system130 associated with a first user. In particular embodiments, the firstuser may be a user of an online social network. At step 720, theassistant system 140 may access a plurality of user communications fromthe first content source. At step 730, the assistant system 140 mayidentify a plurality of segments associated with the plurality of usercommunications. In particular embodiments, the plurality of segments maybe associated with a plurality of topics, respectively. At step 740, theassistant system 140 may calculate, for each segment of the plurality ofsegments, a user interest score for the segment. In particularembodiments, the user interest score may be based on a user profile ofthe first user. At step 750, the assistant system 140 may select one ormore of the segments for summarization based on their user interestscores. At step 760, the assistant system 140 may generate one or morepersonalized summaries of the one or more selected segments. Inparticular embodiments, the personalization of the summary may be basedon the user profile of the first user. At step 770, the assistant system140 may send instructions to present the personalized summaries to thefirst user responsive to the request. Particular embodiments may repeatone or more steps of the method of FIG. 7, where appropriate. Althoughthis disclosure describes and illustrates particular steps of the methodof FIG. 7 as occurring in a particular order, this disclosurecontemplates any suitable steps of the method of FIG. 7 occurring in anysuitable order. Moreover, although this disclosure describes andillustrates an example method for generating summaries by an assistantsystem 140 for a content source including the particular steps of themethod of FIG. 7, this disclosure contemplates any suitable method forgenerating summaries by an assistant system 140 for a content sourceincluding any suitable steps, which may include all, some, or none ofthe steps of the method of FIG. 7, where appropriate. Furthermore,although this disclosure describes and illustrates particularcomponents, devices, or systems carrying out particular steps of themethod of FIG. 7, this disclosure contemplates any suitable combinationof any suitable components, devices, or systems carrying out anysuitable steps of the method of FIG. 7.

Social Graphs

FIG. 8 illustrates an example social graph 800. In particularembodiments, the social-networking system 160 may store one or moresocial graphs 800 in one or more data stores. In particular embodiments,the social graph 800 may include multiple nodes—which may includemultiple user nodes 802 or multiple concept nodes 804—and multiple edges806 connecting the nodes. Each node may be associated with a uniqueentity (i.e., user or concept), each of which may have a uniqueidentifier (ID), such as a unique number or username. The example socialgraph 800 illustrated in FIG. 8 is shown, for didactic purposes, in atwo-dimensional visual map representation. In particular embodiments, asocial-networking system 160, a client system 130, an assistant system140, or a third-party system 170 may access the social graph 800 andrelated social-graph information for suitable applications. The nodesand edges of the social graph 800 may be stored as data objects, forexample, in a data store (such as a social-graph database). Such a datastore may include one or more searchable or queryable indexes of nodesor edges of the social graph 800.

In particular embodiments, a user node 802 may correspond to a user ofthe social-networking system 160 or the assistant system 140. As anexample and not by way of limitation, a user may be an individual (humanuser), an entity (e.g., an enterprise, business, or third-partyapplication), or a group (e.g., of individuals or entities) thatinteracts or communicates with or over the social-networking system 160or the assistant system 140. In particular embodiments, when a userregisters for an account with the social-networking system 160, thesocial-networking system 160 may create a user node 802 corresponding tothe user, and store the user node 802 in one or more data stores. Usersand user nodes 802 described herein may, where appropriate, refer toregistered users and user nodes 802 associated with registered users. Inaddition or as an alternative, users and user nodes 802 described hereinmay, where appropriate, refer to users that have not registered with thesocial-networking system 160. In particular embodiments, a user node 802may be associated with information provided by a user or informationgathered by various systems, including the social-networking system 160.As an example and not by way of limitation, a user may provide his orher name, profile picture, contact information, birth date, sex, maritalstatus, family status, employment, education background, preferences,interests, or other demographic information. In particular embodiments,a user node 802 may be associated with one or more data objectscorresponding to information associated with a user. In particularembodiments, a user node 802 may correspond to one or more webinterfaces.

In particular embodiments, a concept node 804 may correspond to aconcept. As an example and not by way of limitation, a concept maycorrespond to a place (such as, for example, a movie theater,restaurant, landmark, or city); a website (such as, for example, awebsite associated with the social-networking system 160 or athird-party website associated with a web-application server); an entity(such as, for example, a person, business, group, sports team, orcelebrity); a resource (such as, for example, an audio file, video file,digital photo, text file, structured document, or application) which maybe located within the social-networking system 160 or on an externalserver, such as a web-application server; real or intellectual property(such as, for example, a sculpture, painting, movie, game, song, idea,photograph, or written work); a game; an activity; an idea or theory;another suitable concept; or two or more such concepts. A concept node804 may be associated with information of a concept provided by a useror information gathered by various systems, including thesocial-networking system 160 and the assistant system 140. As an exampleand not by way of limitation, information of a concept may include aname or a title; one or more images (e.g., an image of the cover page ofa book); a location (e.g., an address or a geographical location); awebsite (which may be associated with a URL); contact information (e.g.,a phone number or an email address); other suitable concept information;or any suitable combination of such information. In particularembodiments, a concept node 804 may be associated with one or more dataobjects corresponding to information associated with concept node 804.In particular embodiments, a concept node 804 may correspond to one ormore web interfaces.

In particular embodiments, a node in the social graph 800 may representor be represented by a web interface (which may be referred to as a“profile interface”). Profile interfaces may be hosted by or accessibleto the social-networking system 160 or the assistant system 140. Profileinterfaces may also be hosted on third-party websites associated with athird-party system 170. As an example and not by way of limitation, aprofile interface corresponding to a particular external web interfacemay be the particular external web interface and the profile interfacemay correspond to a particular concept node 804. Profile interfaces maybe viewable by all or a selected subset of other users. As an exampleand not by way of limitation, a user node 802 may have a correspondinguser-profile interface in which the corresponding user may add content,make declarations, or otherwise express himself or herself. As anotherexample and not by way of limitation, a concept node 804 may have acorresponding concept-profile interface in which one or more users mayadd content, make declarations, or express themselves, particularly inrelation to the concept corresponding to concept node 804.

In particular embodiments, a concept node 804 may represent athird-party web interface or resource hosted by a third-party system170. The third-party web interface or resource may include, among otherelements, content, a selectable or other icon, or other inter-actableobject representing an action or activity. As an example and not by wayof limitation, a third-party web interface may include a selectable iconsuch as “like,” “check-in,” “eat,” “recommend,” or another suitableaction or activity. A user viewing the third-party web interface mayperform an action by selecting one of the icons (e.g., “check-in”),causing a client system 130 to send to the social-networking system 160a message indicating the user's action. In response to the message, thesocial-networking system 160 may create an edge (e.g., a check-in-typeedge) between a user node 802 corresponding to the user and a conceptnode 804 corresponding to the third-party web interface or resource andstore edge 806 in one or more data stores.

In particular embodiments, a pair of nodes in the social graph 800 maybe connected to each other by one or more edges 806. An edge 806connecting a pair of nodes may represent a relationship between the pairof nodes. In particular embodiments, an edge 806 may include orrepresent one or more data objects or attributes corresponding to therelationship between a pair of nodes. As an example and not by way oflimitation, a first user may indicate that a second user is a “friend”of the first user. In response to this indication, the social-networkingsystem 160 may send a “friend request” to the second user. If the seconduser confirms the “friend request,” the social-networking system 160 maycreate an edge 806 connecting the first user's user node 802 to thesecond user's user node 802 in the social graph 800 and store edge 806as social-graph information in one or more of data stores 164. In theexample of FIG. 8, the social graph 800 includes an edge 806 indicatinga friend relation between user nodes 802 of user “A” and user “B” and anedge indicating a friend relation between user nodes 802 of user “C” anduser “B.” Although this disclosure describes or illustrates particularedges 806 with particular attributes connecting particular user nodes802, this disclosure contemplates any suitable edges 806 with anysuitable attributes connecting user nodes 802. As an example and not byway of limitation, an edge 806 may represent a friendship, familyrelationship, business or employment relationship, fan relationship(including, e.g., liking, etc.), follower relationship, visitorrelationship (including, e.g., accessing, viewing, checking-in, sharing,etc.), subscriber relationship, superior/subordinate relationship,reciprocal relationship, non-reciprocal relationship, another suitabletype of relationship, or two or more such relationships. Moreover,although this disclosure generally describes nodes as being connected,this disclosure also describes users or concepts as being connected.Herein, references to users or concepts being connected may, whereappropriate, refer to the nodes corresponding to those users or conceptsbeing connected in the social graph 800 by one or more edges 806.

In particular embodiments, an edge 806 between a user node 802 and aconcept node 804 may represent a particular action or activity performedby a user associated with user node 802 toward a concept associated witha concept node 804. As an example and not by way of limitation, asillustrated in FIG. 8, a user may “like,” “attended,” “played,”“listened,” “cooked,” “worked at,” or “watched” a concept, each of whichmay correspond to an edge type or subtype. A concept-profile interfacecorresponding to a concept node 804 may include, for example, aselectable “check in” icon (such as, for example, a clickable “check in”icon) or a selectable “add to favorites” icon. Similarly, after a userclicks these icons, the social-networking system 160 may create a“favorite” edge or a “check in” edge in response to a user's actioncorresponding to a respective action. As another example and not by wayof limitation, a user (user “C”) may listen to a particular song(“Imagine”) using a particular application (an online musicapplication). In this case, the social-networking system 160 may createa “listened” edge 806 and a “used” edge (as illustrated in FIG. 8)between user nodes 802 corresponding to the user and concept nodes 804corresponding to the song and application to indicate that the userlistened to the song and used the application. Moreover, thesocial-networking system 160 may create a “played” edge 806 (asillustrated in FIG. 8) between concept nodes 804 corresponding to thesong and the application to indicate that the particular song was playedby the particular application. In this case, “played” edge 806corresponds to an action performed by an external application on anexternal audio file (the song “Imagine”). Although this disclosuredescribes particular edges 806 with particular attributes connectinguser nodes 802 and concept nodes 804, this disclosure contemplates anysuitable edges 806 with any suitable attributes connecting user nodes802 and concept nodes 804. Moreover, although this disclosure describesedges between a user node 802 and a concept node 804 representing asingle relationship, this disclosure contemplates edges between a usernode 802 and a concept node 804 representing one or more relationships.As an example and not by way of limitation, an edge 806 may representboth that a user likes and has used at a particular concept.Alternatively, another edge 806 may represent each type of relationship(or multiples of a single relationship) between a user node 802 and aconcept node 804 (as illustrated in FIG. 8 between user node 802 foruser “E” and concept node 804).

In particular embodiments, the social-networking system 160 may createan edge 806 between a user node 802 and a concept node 804 in the socialgraph 800. As an example and not by way of limitation, a user viewing aconcept-profile interface (such as, for example, by using a web browseror a special-purpose application hosted by the user's client system 130)may indicate that he or she likes the concept represented by the conceptnode 804 by clicking or selecting a “Like” icon, which may cause theuser's client system 130 to send to the social-networking system 160 amessage indicating the user's liking of the concept associated with theconcept-profile interface. In response to the message, thesocial-networking system 160 may create an edge 806 between user node802 associated with the user and concept node 804, as illustrated by“like” edge 806 between the user and concept node 804. In particularembodiments, the social-networking system 160 may store an edge 806 inone or more data stores. In particular embodiments, an edge 806 may beautomatically formed by the social-networking system 160 in response toa particular user action. As an example and not by way of limitation, ifa first user uploads a picture, watches a movie, or listens to a song,an edge 806 may be formed between user node 802 corresponding to thefirst user and concept nodes 804 corresponding to those concepts.Although this disclosure describes forming particular edges 806 inparticular manners, this disclosure contemplates forming any suitableedges 806 in any suitable manner.

Vector Spaces and Embeddings

FIG. 9 illustrates an example view of a vector space 900. In particularembodiments, an object or an n-gram may be represented in ad-dimensional vector space, where d denotes any suitable number ofdimensions. Although the vector space 900 is illustrated as athree-dimensional space, this is for illustrative purposes only, as thevector space 900 may be of any suitable dimension. In particularembodiments, an n-gram may be represented in the vector space 900 as avector referred to as a term embedding. Each vector may comprisecoordinates corresponding to a particular point in the vector space 900(i.e., the terminal point of the vector). As an example and not by wayof limitation, vectors 910, 920, and 930 may be represented as points inthe vector space 900, as illustrated in FIG. 9. An n-gram may be mappedto a respective vector representation. As an example and not by way oflimitation, n-grams t₁ and t₂ may be mapped to vectors v₁

and v₂

in the vector space 900, respectively, by applying a function π

defined by a dictionary, such that v₁

=π

(t₁) and v₂

=π

(t₂). As another example and not by way of limitation, a dictionarytrained to map text to a vector representation may be utilized, or sucha dictionary may be itself generated via training. As another exampleand not by way of limitation, a model, such as Word2vec, may be used tomap an n-gram to a vector representation in the vector space 900. Inparticular embodiments, an n-gram may be mapped to a vectorrepresentation in the vector space 900 by using a machine leaning model(e.g., a neural network). The machine learning model may have beentrained using a sequence of training data (e.g., a corpus of objectseach comprising n-grams).

In particular embodiments, an object may be represented in the vectorspace 900 as a vector referred to as a feature vector or an objectembedding. As an example and not by way of limitation, objects e₁ and e₂may be mapped to vectors v₁

and v₂

in the vector space 900, respectively, by applying a function π

, such that v₁

=π

(e₁) and v₂

=π

(e₂). In particular embodiments, an object may be mapped to a vectorbased on one or more properties, attributes, or features of the object,relationships of the object with other objects, or any other suitableinformation associated with the object. As an example and not by way oflimitation, a function π

may map objects to vectors by feature extraction, which may start froman initial set of measured data and build derived values (e.g.,features). As an example and not by way of limitation, an objectcomprising a video or an image may be mapped to a vector by using analgorithm to detect or isolate various desired portions or shapes of theobject. Features used to calculate the vector may be based oninformation obtained from edge detection, corner detection, blobdetection, ridge detection, scale-invariant feature transformation, edgedirection, changing intensity, autocorrelation, motion detection,optical flow, thresholding, blob extraction, template matching, Houghtransformation (e.g., lines, circles, ellipses, arbitrary shapes), orany other suitable information. As another example and not by way oflimitation, an object comprising audio data may be mapped to a vectorbased on features such as a spectral slope, a tonality coefficient, anaudio spectrum centroid, an audio spectrum envelope, a Mel-frequencycepstrum, or any other suitable information. In particular embodiments,when an object has data that is either too large to be efficientlyprocessed or comprises redundant data, a function π

may map the object to a vector using a transformed reduced set offeatures (e.g., feature selection). In particular embodiments, afunction π

may map an object e to a vector π

(e) based on one or more n-grams associated with object e. Although thisdisclosure describes representing an n-gram or an object in a vectorspace in a particular manner, this disclosure contemplates representingan n-gram or an object in a vector space in any suitable manner.

In particular embodiments, the social-networking system 160 maycalculate a similarity metric of vectors in vector space 900. Asimilarity metric may be a cosine similarity, a Minkowski distance, aMahalanobis distance, a Jaccard similarity coefficient, or any suitablesimilarity metric. As an example and not by way of limitation, asimilarity metric of v₁

and v₂

may be a cosine similarity

$\frac{v_{1}^{\rightharpoonup} \cdot v_{2}^{\rightharpoonup}}{{v_{1}^{\rightharpoonup}}{v_{2}^{\rightharpoonup}}}.$As another example and not by way of limitation, a similarity metric ofv₁

and v₂

may be a Euclidean distance ∥v₁

−v₂

∥. A similarity metric of two vectors may represent how similar the twoobjects or n-grams corresponding to the two vectors, respectively, areto one another, as measured by the distance between the two vectors inthe vector space 900. As an example and not by way of limitation, vector910 and vector 920 may correspond to objects that are more similar toone another than the objects corresponding to vector 910 and vector 930,based on the distance between the respective vectors. Although thisdisclosure describes calculating a similarity metric between vectors ina particular manner, this disclosure contemplates calculating asimilarity metric between vectors in any suitable manner.

More information on vector spaces, embeddings, feature vectors, andsimilarity metrics may be found in U.S. patent application Ser. No.14/949,436, filed 23 Nov. 2015, U.S. patent application Ser. No.15/286,315, filed 5 Oct. 2016, and U.S. patent application Ser. No.15/365,789, filed 30 Nov. 2016, each of which is incorporated byreference.

Artificial Neural Networks

FIG. 10 illustrates an example artificial neural network (“ANN”) 1000.In particular embodiments, an ANN may refer to a computational modelcomprising one or more nodes. Example ANN 1000 may comprise an inputlayer 1010, hidden layers 1020, 1030, 1060, and an output layer 1050.Each layer of the ANN 1000 may comprise one or more nodes, such as anode 1005 or a node 1015. In particular embodiments, each node of an ANNmay be connected to another node of the ANN. As an example and not byway of limitation, each node of the input layer 1010 may be connected toone of more nodes of the hidden layer 1020. In particular embodiments,one or more nodes may be a bias node (e.g., a node in a layer that isnot connected to and does not receive input from any node in a previouslayer). In particular embodiments, each node in each layer may beconnected to one or more nodes of a previous or subsequent layer.Although FIG. 10 depicts a particular ANN with a particular number oflayers, a particular number of nodes, and particular connections betweennodes, this disclosure contemplates any suitable ANN with any suitablenumber of layers, any suitable number of nodes, and any suitableconnections between nodes. As an example and not by way of limitation,although FIG. 10 depicts a connection between each node of the inputlayer 1010 and each node of the hidden layer 1020, one or more nodes ofthe input layer 1010 may not be connected to one or more nodes of thehidden layer 1020.

In particular embodiments, an ANN may be a feedforward ANN (e.g., an ANNwith no cycles or loops where communication between nodes flows in onedirection beginning with the input layer and proceeding to successivelayers). As an example and not by way of limitation, the input to eachnode of the hidden layer 1020 may comprise the output of one or morenodes of the input layer 1010. As another example and not by way oflimitation, the input to each node of the output layer 1050 may comprisethe output of one or more nodes of the hidden layer 1060. In particularembodiments, an ANN may be a deep neural network (e.g., a neural networkcomprising at least two hidden layers). In particular embodiments, anANN may be a deep residual network. A deep residual network may be afeedforward ANN comprising hidden layers organized into residual blocks.The input into each residual block after the first residual block may bea function of the output of the previous residual block and the input ofthe previous residual block. As an example and not by way of limitation,the input into residual block N may be F(x)+x, where F(x) may be theoutput of residual block N −1, x may be the input into residual block N−1. Although this disclosure describes a particular ANN, this disclosurecontemplates any suitable ANN.

In particular embodiments, an activation function may correspond to eachnode of an ANN. An activation function of a node may define the outputof a node for a given input. In particular embodiments, an input to anode may comprise a set of inputs. As an example and not by way oflimitation, an activation function may be an identity function, a binarystep function, a logistic function, or any other suitable function. Asanother example and not by way of limitation, an activation function fora node k may be the sigmoid function

${{F_{k}\left( s_{k} \right)} = \frac{1}{1 + e^{- s_{k}}}},$the hyperbolic tangent function

${{F_{k}\left( s_{k} \right)} = \frac{e^{s_{k}} - e^{- s_{k}}}{e^{s_{k}} + e^{- s_{k}}}},$the rectifier F_(k)(s_(k))=max(0,s_(k)), or any other suitable functionF_(k)(s_(k)), where s_(k) may be the effective input to node k. Inparticular embodiments, the input of an activation functioncorresponding to a node may be weighted. Each node may generate outputusing a corresponding activation function based on weighted inputs. Inparticular embodiments, each connection between nodes may be associatedwith a weight. As an example and not by way of limitation, a connection1025 between the node 1005 and the node 1015 may have a weightingcoefficient of 0.4, which may indicate that 0.4 multiplied by the outputof the node 1005 is used as an input to the node 1015. As anotherexample and not by way of limitation, the output y_(k) of node k may bey_(k)=F_(k)(s_(k)), where F_(k) may be the activation functioncorresponding to node k, s_(k)=Σ_(j) (w_(jk)x_(j)) may be the effectiveinput to node k, x_(j) may be the output of a node j connected to nodek, and w_(jk) may be the weighting coefficient between node j and nodek. In particular embodiments, the input to nodes of the input layer maybe based on a vector representing an object. Although this disclosuredescribes particular inputs to and outputs of nodes, this disclosurecontemplates any suitable inputs to and outputs of nodes. Moreover,although this disclosure may describe particular connections and weightsbetween nodes, this disclosure contemplates any suitable connections andweights between nodes.

In particular embodiments, an ANN may be trained using training data. Asan example and not by way of limitation, training data may compriseinputs to the ANN 1000 and an expected output. As another example andnot by way of limitation, training data may comprise vectors eachrepresenting a training object and an expected label for each trainingobject. In particular embodiments, training an ANN may comprisemodifying the weights associated with the connections between nodes ofthe ANN by optimizing an objective function. As an example and not byway of limitation, a training method may be used (e.g., the conjugategradient method, the gradient descent method, the stochastic gradientdescent) to backpropagate the sum-of-squares error measured as adistances between each vector representing a training object (e.g.,using a cost function that minimizes the sum-of-squares error). Inparticular embodiments, an ANN may be trained using a dropout technique.As an example and not by way of limitation, one or more nodes may betemporarily omitted (e.g., receive no input and generate no output)while training. For each training object, one or more nodes of the ANNmay have some probability of being omitted. The nodes that are omittedfor a particular training object may be different than the nodes omittedfor other training objects (e.g., the nodes may be temporarily omittedon an object-by-object basis). Although this disclosure describestraining an ANN in a particular manner, this disclosure contemplatestraining an ANN in any suitable manner.

Privacy

In particular embodiments, one or more objects (e.g., content or othertypes of objects) of a computing system may be associated with one ormore privacy settings. The one or more objects may be stored on orotherwise associated with any suitable computing system or application,such as, for example, a social-networking system 160, a client system130, an assistant system 140, a third-party system 170, asocial-networking application, an assistant application, a messagingapplication, a photo-sharing application, or any other suitablecomputing system or application. Although the examples discussed hereinare in the context of an online social network, these privacy settingsmay be applied to any other suitable computing system. Privacy settings(or “access settings”) for an object may be stored in any suitablemanner, such as, for example, in association with the object, in anindex on an authorization server, in another suitable manner, or anysuitable combination thereof. A privacy setting for an object mayspecify how the object (or particular information associated with theobject) can be accessed, stored, or otherwise used (e.g., viewed,shared, modified, copied, executed, surfaced, or identified) within theonline social network. When privacy settings for an object allow aparticular user or other entity to access that object, the object may bedescribed as being “visible” with respect to that user or other entity.As an example and not by way of limitation, a user of the online socialnetwork may specify privacy settings for a user-profile page thatidentify a set of users that may access work-experience information onthe user-profile page, thus excluding other users from accessing thatinformation.

In particular embodiments, privacy settings for an object may specify a“blocked list” of users or other entities that should not be allowed toaccess certain information associated with the object. In particularembodiments, the blocked list may include third-party entities. Theblocked list may specify one or more users or entities for which anobject is not visible. As an example and not by way of limitation, auser may specify a set of users who may not access photo albumsassociated with the user, thus excluding those users from accessing thephoto albums (while also possibly allowing certain users not within thespecified set of users to access the photo albums). In particularembodiments, privacy settings may be associated with particularsocial-graph elements. Privacy settings of a social-graph element, suchas a node or an edge, may specify how the social-graph element,information associated with the social-graph element, or objectsassociated with the social-graph element can be accessed using theonline social network. As an example and not by way of limitation, aparticular concept node 804 corresponding to a particular photo may havea privacy setting specifying that the photo may be accessed only byusers tagged in the photo and friends of the users tagged in the photo.In particular embodiments, privacy settings may allow users to opt in toor opt out of having their content, information, or actionsstored/logged by the social-networking system 160 or assistant system140 or shared with other systems (e.g., a third-party system 170).Although this disclosure describes using particular privacy settings ina particular manner, this disclosure contemplates using any suitableprivacy settings in any suitable manner.

In particular embodiments, privacy settings may be based on one or morenodes or edges of a social graph 800. A privacy setting may be specifiedfor one or more edges 806 or edge-types of the social graph 800, or withrespect to one or more nodes 802, 804 or node-types of the social graph800. The privacy settings applied to a particular edge 806 connectingtwo nodes may control whether the relationship between the two entitiescorresponding to the nodes is visible to other users of the onlinesocial network. Similarly, the privacy settings applied to a particularnode may control whether the user or concept corresponding to the nodeis visible to other users of the online social network. As an exampleand not by way of limitation, a first user may share an object to thesocial-networking system 160. The object may be associated with aconcept node 804 connected to a user node 802 of the first user by anedge 806. The first user may specify privacy settings that apply to aparticular edge 806 connecting to the concept node 804 of the object, ormay specify privacy settings that apply to all edges 806 connecting tothe concept node 804. As another example and not by way of limitation,the first user may share a set of objects of a particular object-type(e.g., a set of images). The first user may specify privacy settingswith respect to all objects associated with the first user of thatparticular object-type as having a particular privacy setting (e.g.,specifying that all images posted by the first user are visible only tofriends of the first user and/or users tagged in the images).

In particular embodiments, the social-networking system 160 may presenta “privacy wizard” (e.g., within a webpage, a module, one or more dialogboxes, or any other suitable interface) to the first user to assist thefirst user in specifying one or more privacy settings. The privacywizard may display instructions, suitable privacy-related information,current privacy settings, one or more input fields for accepting one ormore inputs from the first user specifying a change or confirmation ofprivacy settings, or any suitable combination thereof. In particularembodiments, the social-networking system 160 may offer a “dashboard”functionality to the first user that may display, to the first user,current privacy settings of the first user. The dashboard functionalitymay be displayed to the first user at any appropriate time (e.g.,following an input from the first user summoning the dashboardfunctionality, following the occurrence of a particular event or triggeraction). The dashboard functionality may allow the first user to modifyone or more of the first user's current privacy settings at any time, inany suitable manner (e.g., redirecting the first user to the privacywizard).

Privacy settings associated with an object may specify any suitablegranularity of permitted access or denial of access. As an example andnot by way of limitation, access or denial of access may be specifiedfor particular users (e.g., only me, my roommates, my boss), userswithin a particular degree-of-separation (e.g., friends,friends-of-friends), user groups (e.g., the gaming club, my family),user networks (e.g., employees of particular employers, students oralumni of particular university), all users (“public”), no users(“private”), users of third-party systems 170, particular applications(e.g., third-party applications, external websites), other suitableentities, or any suitable combination thereof. Although this disclosuredescribes particular granularities of permitted access or denial ofaccess, this disclosure contemplates any suitable granularities ofpermitted access or denial of access.

In particular embodiments, one or more servers 162 may beauthorization/privacy servers for enforcing privacy settings. Inresponse to a request from a user (or other entity) for a particularobject stored in a data store 164, the social-networking system 160 maysend a request to the data store 164 for the object. The request mayidentify the user associated with the request and the object may be sentonly to the user (or a client system 130 of the user) if theauthorization server determines that the user is authorized to accessthe object based on the privacy settings associated with the object. Ifthe requesting user is not authorized to access the object, theauthorization server may prevent the requested object from beingretrieved from the data store 164 or may prevent the requested objectfrom being sent to the user. In the search-query context, an object maybe provided as a search result only if the querying user is authorizedto access the object, e.g., if the privacy settings for the object allowit to be surfaced to, discovered by, or otherwise visible to thequerying user. In particular embodiments, an object may representcontent that is visible to a user through a newsfeed of the user. As anexample and not by way of limitation, one or more objects may be visibleto a user's “Trending” page. In particular embodiments, an object maycorrespond to a particular user. The object may be content associatedwith the particular user, or may be the particular user's account orinformation stored on the social-networking system 160, or othercomputing system. As an example and not by way of limitation, a firstuser may view one or more second users of an online social networkthrough a “People You May Know” function of the online social network,or by viewing a list of friends of the first user. As an example and notby way of limitation, a first user may specify that they do not wish tosee objects associated with a particular second user in their newsfeedor friends list. If the privacy settings for the object do not allow itto be surfaced to, discovered by, or visible to the user, the object maybe excluded from the search results. Although this disclosure describesenforcing privacy settings in a particular manner, this disclosurecontemplates enforcing privacy settings in any suitable manner.

In particular embodiments, different objects of the same type associatedwith a user may have different privacy settings. Different types ofobjects associated with a user may have different types of privacysettings. As an example and not by way of limitation, a first user mayspecify that the first user's status updates are public, but any imagesshared by the first user are visible only to the first user's friends onthe online social network. As another example and not by way oflimitation, a user may specify different privacy settings for differenttypes of entities, such as individual users, friends-of-friends,followers, user groups, or corporate entities. As another example andnot by way of limitation, a first user may specify a group of users thatmay view videos posted by the first user, while keeping the videos frombeing visible to the first user's employer. In particular embodiments,different privacy settings may be provided for different user groups oruser demographics. As an example and not by way of limitation, a firstuser may specify that other users who attend the same university as thefirst user may view the first user's pictures, but that other users whoare family members of the first user may not view those same pictures.

In particular embodiments, the social-networking system 160 may provideone or more default privacy settings for each object of a particularobject-type. A privacy setting for an object that is set to a defaultmay be changed by a user associated with that object. As an example andnot by way of limitation, all images posted by a first user may have adefault privacy setting of being visible only to friends of the firstuser and, for a particular image, the first user may change the privacysetting for the image to be visible to friends and friends-of-friends.

In particular embodiments, privacy settings may allow a first user tospecify (e.g., by opting out, by not opting in) whether thesocial-networking system 160 or assistant system 140 may receive,collect, log, or store particular objects or information associated withthe user for any purpose. In particular embodiments, privacy settingsmay allow the first user to specify whether particular applications orprocesses may access, store, or use particular objects or informationassociated with the user. The privacy settings may allow the first userto opt in or opt out of having objects or information accessed, stored,or used by specific applications or processes. The social-networkingsystem 160 or assistant system 140 may access such information in orderto provide a particular function or service to the first user, withoutthe social-networking system 160 or assistant system 140 having accessto that information for any other purposes. Before accessing, storing,or using such objects or information, the social-networking system 160or assistant system 140 may prompt the user to provide privacy settingsspecifying which applications or processes, if any, may access, store,or use the object or information prior to allowing any such action. Asan example and not by way of limitation, a first user may transmit amessage to a second user via an application related to the online socialnetwork (e.g., a messaging app), and may specify privacy settings thatsuch messages should not be stored by the social-networking system 160or assistant system 140.

In particular embodiments, a user may specify whether particular typesof objects or information associated with the first user may beaccessed, stored, or used by the social-networking system 160 orassistant system 140. As an example and not by way of limitation, thefirst user may specify that images sent by the first user through thesocial-networking system 160 or assistant system 140 may not be storedby the social-networking system 160 or assistant system 140. As anotherexample and not by way of limitation, a first user may specify thatmessages sent from the first user to a particular second user may not bestored by the social-networking system 160 or assistant system 140. Asyet another example and not by way of limitation, a first user mayspecify that all objects sent via a particular application may be savedby the social-networking system 160 or assistant system 140.

In particular embodiments, privacy settings may allow a first user tospecify whether particular objects or information associated with thefirst user may be accessed from particular client systems 130 orthird-party systems 170. The privacy settings may allow the first userto opt in or opt out of having objects or information accessed from aparticular device (e.g., the phone book on a user's smart phone), from aparticular application (e.g., a messaging app), or from a particularsystem (e.g., an email server). The social-networking system 160 orassistant system 140 may provide default privacy settings with respectto each device, system, or application, and/or the first user may beprompted to specify a particular privacy setting for each context. As anexample and not by way of limitation, the first user may utilize alocation-services feature of the social-networking system 160 orassistant system 140 to provide recommendations for restaurants or otherplaces in proximity to the user. The first user's default privacysettings may specify that the social-networking system 160 or assistantsystem 140 may use location information provided from a client device130 of the first user to provide the location-based services, but thatthe social-networking system 160 or assistant system 140 may not storethe location information of the first user or provide it to anythird-party system 170. The first user may then update the privacysettings to allow location information to be used by a third-partyimage-sharing application in order to geo-tag photos.

In particular embodiments, privacy settings may allow a user to specifyone or more geographic locations from which objects can be accessed.Access or denial of access to the objects may depend on the geographiclocation of a user who is attempting to access the objects. As anexample and not by way of limitation, a user may share an object andspecify that only users in the same city may access or view the object.As another example and not by way of limitation, a first user may sharean object and specify that the object is visible to second users onlywhile the first user is in a particular location. If the first userleaves the particular location, the object may no longer be visible tothe second users. As another example and not by way of limitation, afirst user may specify that an object is visible only to second userswithin a threshold distance from the first user. If the first usersubsequently changes location, the original second users with access tothe object may lose access, while a new group of second users may gainaccess as they come within the threshold distance of the first user.

In particular embodiments, the social-networking system 160 or assistantsystem 140 may have functionalities that may use, as inputs, personal orbiometric information of a user for user-authentication orexperience-personalization purposes. A user may opt to make use of thesefunctionalities to enhance their experience on the online socialnetwork. As an example and not by way of limitation, a user may providepersonal or biometric information to the social-networking system 160 orassistant system 140. The user's privacy settings may specify that suchinformation may be used only for particular processes, such asauthentication, and further specify that such information may not beshared with any third-party system 170 or used for other processes orapplications associated with the social-networking system 160 orassistant system 140. As another example and not by way of limitation,the social-networking system 160 may provide a functionality for a userto provide voice-print recordings to the online social network. As anexample and not by way of limitation, if a user wishes to utilize thisfunction of the online social network, the user may provide a voicerecording of his or her own voice to provide a status update on theonline social network. The recording of the voice-input may be comparedto a voice print of the user to determine what words were spoken by theuser. The user's privacy setting may specify that such voice recordingmay be used only for voice-input purposes (e.g., to authenticate theuser, to send voice messages, to improve voice recognition in order touse voice-operated features of the online social network), and furtherspecify that such voice recording may not be shared with any third-partysystem 170 or used by other processes or applications associated withthe social-networking system 160. As another example and not by way oflimitation, the social-networking system 160 may provide a functionalityfor a user to provide a reference image (e.g., a facial profile, aretinal scan) to the online social network. The online social networkmay compare the reference image against a later-received image input(e.g., to authenticate the user, to tag the user in photos). The user'sprivacy setting may specify that such voice recording may be used onlyfor a limited purpose (e.g., authentication, tagging the user inphotos), and further specify that such voice recording may not be sharedwith any third-party system 170 or used by other processes orapplications associated with the social-networking system 160.

Systems and Methods

FIG. 11 illustrates an example computer system 1100. In particularembodiments, one or more computer systems 1100 perform one or more stepsof one or more methods described or illustrated herein. In particularembodiments, one or more computer systems 1100 provide functionalitydescribed or illustrated herein. In particular embodiments, softwarerunning on one or more computer systems 1100 performs one or more stepsof one or more methods described or illustrated herein or providesfunctionality described or illustrated herein. Particular embodimentsinclude one or more portions of one or more computer systems 1100.Herein, reference to a computer system may encompass a computing device,and vice versa, where appropriate. Moreover, reference to a computersystem may encompass one or more computer systems, where appropriate.

This disclosure contemplates any suitable number of computer systems1100. This disclosure contemplates computer system 1100 taking anysuitable physical form. As example and not by way of limitation,computer system 1100 may be an embedded computer system, asystem-on-chip (SOC), a single-board computer system (SBC) (such as, forexample, a computer-on-module (COM) or system-on-module (SOM)), adesktop computer system, a laptop or notebook computer system, aninteractive kiosk, a mainframe, a mesh of computer systems, a mobiletelephone, a personal digital assistant (PDA), a server, a tabletcomputer system, or a combination of two or more of these. Whereappropriate, computer system 1100 may include one or more computersystems 1100; be unitary or distributed; span multiple locations; spanmultiple machines; span multiple data centers; or reside in a cloud,which may include one or more cloud components in one or more networks.Where appropriate, one or more computer systems 1100 may perform withoutsubstantial spatial or temporal limitation one or more steps of one ormore methods described or illustrated herein. As an example and not byway of limitation, one or more computer systems 1100 may perform in realtime or in batch mode one or more steps of one or more methods describedor illustrated herein. One or more computer systems 1100 may perform atdifferent times or at different locations one or more steps of one ormore methods described or illustrated herein, where appropriate.

In particular embodiments, computer system 1100 includes a processor1102, memory 1104, storage 1106, an input/output (I/O) interface 1108, acommunication interface 1110, and a bus 1112. Although this disclosuredescribes and illustrates a particular computer system having aparticular number of particular components in a particular arrangement,this disclosure contemplates any suitable computer system having anysuitable number of any suitable components in any suitable arrangement.

In particular embodiments, processor 1102 includes hardware forexecuting instructions, such as those making up a computer program. Asan example and not by way of limitation, to execute instructions,processor 1102 may retrieve (or fetch) the instructions from an internalregister, an internal cache, memory 1104, or storage 1106; decode andexecute them; and then write one or more results to an internalregister, an internal cache, memory 1104, or storage 1106. In particularembodiments, processor 1102 may include one or more internal caches fordata, instructions, or addresses. This disclosure contemplates processor1102 including any suitable number of any suitable internal caches,where appropriate. As an example and not by way of limitation, processor1102 may include one or more instruction caches, one or more datacaches, and one or more translation lookaside buffers (TLBs).Instructions in the instruction caches may be copies of instructions inmemory 1104 or storage 1106, and the instruction caches may speed upretrieval of those instructions by processor 1102. Data in the datacaches may be copies of data in memory 1104 or storage 1106 forinstructions executing at processor 1102 to operate on; the results ofprevious instructions executed at processor 1102 for access bysubsequent instructions executing at processor 1102 or for writing tomemory 1104 or storage 1106; or other suitable data. The data caches mayspeed up read or write operations by processor 1102. The TLBs may speedup virtual-address translation for processor 1102. In particularembodiments, processor 1102 may include one or more internal registersfor data, instructions, or addresses. This disclosure contemplatesprocessor 1102 including any suitable number of any suitable internalregisters, where appropriate. Where appropriate, processor 1102 mayinclude one or more arithmetic logic units (ALUs); be a multi-coreprocessor; or include one or more processors 1102. Although thisdisclosure describes and illustrates a particular processor, thisdisclosure contemplates any suitable processor.

In particular embodiments, memory 1104 includes main memory for storinginstructions for processor 1102 to execute or data for processor 1102 tooperate on. As an example and not by way of limitation, computer system1100 may load instructions from storage 1106 or another source (such as,for example, another computer system 1100) to memory 1104. Processor1102 may then load the instructions from memory 1104 to an internalregister or internal cache. To execute the instructions, processor 1102may retrieve the instructions from the internal register or internalcache and decode them. During or after execution of the instructions,processor 1102 may write one or more results (which may be intermediateor final results) to the internal register or internal cache. Processor1102 may then write one or more of those results to memory 1104. Inparticular embodiments, processor 1102 executes only instructions in oneor more internal registers or internal caches or in memory 1104 (asopposed to storage 1106 or elsewhere) and operates only on data in oneor more internal registers or internal caches or in memory 1104 (asopposed to storage 1106 or elsewhere). One or more memory buses (whichmay each include an address bus and a data bus) may couple processor1102 to memory 1104. Bus 1112 may include one or more memory buses, asdescribed below. In particular embodiments, one or more memorymanagement units (MMUs) reside between processor 1102 and memory 1104and facilitate accesses to memory 1104 requested by processor 1102. Inparticular embodiments, memory 1104 includes random access memory (RAM).This RAM may be volatile memory, where appropriate. Where appropriate,this RAM may be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, whereappropriate, this RAM may be single-ported or multi-ported RAM. Thisdisclosure contemplates any suitable RAM. Memory 1104 may include one ormore memories 1104, where appropriate. Although this disclosuredescribes and illustrates particular memory, this disclosurecontemplates any suitable memory.

In particular embodiments, storage 1106 includes mass storage for dataor instructions. As an example and not by way of limitation, storage1106 may include a hard disk drive (HDD), a floppy disk drive, flashmemory, an optical disc, a magneto-optical disc, magnetic tape, or aUniversal Serial Bus (USB) drive or a combination of two or more ofthese. Storage 1106 may include removable or non-removable (or fixed)media, where appropriate. Storage 1106 may be internal or external tocomputer system 1100, where appropriate. In particular embodiments,storage 1106 is non-volatile, solid-state memory. In particularembodiments, storage 1106 includes read-only memory (ROM). Whereappropriate, this ROM may be mask-programmed ROM, programmable ROM(PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM),electrically alterable ROM (EAROM), or flash memory or a combination oftwo or more of these. This disclosure contemplates mass storage 1106taking any suitable physical form. Storage 1106 may include one or morestorage control units facilitating communication between processor 1102and storage 1106, where appropriate. Where appropriate, storage 1106 mayinclude one or more storages 1106. Although this disclosure describesand illustrates particular storage, this disclosure contemplates anysuitable storage.

In particular embodiments, I/O interface 1108 includes hardware,software, or both, providing one or more interfaces for communicationbetween computer system 1100 and one or more I/O devices. Computersystem 1100 may include one or more of these I/O devices, whereappropriate. One or more of these I/O devices may enable communicationbetween a person and computer system 1100. As an example and not by wayof limitation, an I/O device may include a keyboard, keypad, microphone,monitor, mouse, printer, scanner, speaker, still camera, stylus, tablet,touch screen, trackball, video camera, another suitable I/O device or acombination of two or more of these. An I/O device may include one ormore sensors. This disclosure contemplates any suitable I/O devices andany suitable I/O interfaces 1108 for them. Where appropriate, I/Ointerface 1108 may include one or more device or software driversenabling processor 1102 to drive one or more of these I/O devices. I/Ointerface 1108 may include one or more I/O interfaces 1108, whereappropriate. Although this disclosure describes and illustrates aparticular I/O interface, this disclosure contemplates any suitable I/Ointerface.

In particular embodiments, communication interface 1110 includeshardware, software, or both providing one or more interfaces forcommunication (such as, for example, packet-based communication) betweencomputer system 1100 and one or more other computer systems 1100 or oneor more networks. As an example and not by way of limitation,communication interface 1110 may include a network interface controller(NIC) or network adapter for communicating with an Ethernet or otherwire-based network or a wireless NIC (WNIC) or wireless adapter forcommunicating with a wireless network, such as a WI-FI network. Thisdisclosure contemplates any suitable network and any suitablecommunication interface 1110 for it. As an example and not by way oflimitation, computer system 1100 may communicate with an ad hoc network,a personal area network (PAN), a local area network (LAN), a wide areanetwork (WAN), a metropolitan area network (MAN), or one or moreportions of the Internet or a combination of two or more of these. Oneor more portions of one or more of these networks may be wired orwireless. As an example, computer system 1100 may communicate with awireless PAN (WPAN) (such as, for example, a BLUETOOTH WPAN), a WI-FInetwork, a WI-MAX network, a cellular telephone network (such as, forexample, a Global System for Mobile Communications (GSM) network), orother suitable wireless network or a combination of two or more ofthese. Computer system 1100 may include any suitable communicationinterface 1110 for any of these networks, where appropriate.Communication interface 1110 may include one or more communicationinterfaces 1110, where appropriate. Although this disclosure describesand illustrates a particular communication interface, this disclosurecontemplates any suitable communication interface.

In particular embodiments, bus 1112 includes hardware, software, or bothcoupling components of computer system 1100 to each other. As an exampleand not by way of limitation, bus 1112 may include an AcceleratedGraphics Port (AGP) or other graphics bus, an Enhanced Industry StandardArchitecture (EISA) bus, a front-side bus (FSB), a HYPERTRANSPORT (HT)interconnect, an Industry Standard Architecture (ISA) bus, an INFINIBANDinterconnect, a low-pin-count (LPC) bus, a memory bus, a Micro ChannelArchitecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, aPCI-Express (PCIe) bus, a serial advanced technology attachment (SATA)bus, a Video Electronics Standards Association local (VLB) bus, oranother suitable bus or a combination of two or more of these. Bus 1112may include one or more buses 1112, where appropriate. Although thisdisclosure describes and illustrates a particular bus, this disclosurecontemplates any suitable bus or interconnect.

Herein, a computer-readable non-transitory storage medium or media mayinclude one or more semiconductor-based or other integrated circuits(ICs) (such, as for example, field-programmable gate arrays (FPGAs) orapplication-specific ICs (ASICs)), hard disk drives (HDDs), hybrid harddrives (HHDs), optical discs, optical disc drives (ODDs),magneto-optical discs, magneto-optical drives, floppy diskettes, floppydisk drives (FDDs), magnetic tapes, solid-state drives (SSDs),RAM-drives, SECURE DIGITAL cards or drives, any other suitablecomputer-readable non-transitory storage media, or any suitablecombination of two or more of these, where appropriate. Acomputer-readable non-transitory storage medium may be volatile,non-volatile, or a combination of volatile and non-volatile, whereappropriate.

Miscellaneous

Herein, “or” is inclusive and not exclusive, unless expressly indicatedotherwise or indicated otherwise by context. Therefore, herein, “A or B”means “A, B, or both,” unless expressly indicated otherwise or indicatedotherwise by context. Moreover, “and” is both joint and several, unlessexpressly indicated otherwise or indicated otherwise by context.Therefore, herein, “A and B” means “A and B, jointly or severally,”unless expressly indicated otherwise or indicated otherwise by context.

The scope of this disclosure encompasses all changes, substitutions,variations, alterations, and modifications to the example embodimentsdescribed or illustrated herein that a person having ordinary skill inthe art would comprehend. The scope of this disclosure is not limited tothe example embodiments described or illustrated herein. Moreover,although this disclosure describes and illustrates respectiveembodiments herein as including particular components, elements,feature, functions, operations, or steps, any of these embodiments mayinclude any combination or permutation of any of the components,elements, features, functions, operations, or steps described orillustrated anywhere herein that a person having ordinary skill in theart would comprehend. Furthermore, reference in the appended claims toan apparatus or system or a component of an apparatus or system beingadapted to, arranged to, capable of, configured to, enabled to, operableto, or operative to perform a particular function encompasses thatapparatus, system, component, whether or not it or that particularfunction is activated, turned on, or unlocked, as long as thatapparatus, system, or component is so adapted, arranged, capable,configured, enabled, operable, or operative. Additionally, although thisdisclosure describes or illustrates particular embodiments as providingparticular advantages, particular embodiments may provide none, some, orall of these advantages.

What is claimed is:
 1. A method comprising, by one or more computingsystems: receiving, by an assistant xbot associated with the one or morecomputing systems from a client system associated with a first user, arequest for a summary of user communications from a first contentsource; accessing, by the one or more computing systems, a plurality ofuser communications from the first content source; identifying, by theone or more computing systems, a plurality of segments associated withthe plurality of user communications, wherein the plurality of segmentsis associated with a plurality of topics, respectively; selecting, bythe one or more computing systems, one or more of the segments forsummarization; generating, by the one or more computing systems, one ormore personalized summaries of the one or more selected segments withina separate message thread between the assistant xbot and the first user,wherein the personalization of the summary is based on the user profileof the first user, and wherein each of the one or more personalizedsummaries comprises an interactive element that is selectable to respondto the respective segment; and sending, by the one or more computingsystems to the client system, instructions to present the personalizedsummaries to the first user responsive to the request.
 2. The method ofclaim 1, wherein receiving the request for the summary of usercommunications comprises one or more of receiving a text input, an audioinput, a visual input, or a selection of an activatable element.
 3. Themethod of claim 1, wherein the user communications from the firstcontent source are authored by one or more users of a plurality ofusers, and wherein the plurality of users comprises at least the firstuser.
 4. The method of claim 1, further comprising: determining a timeperiod associated with the request for the summary of usercommunications, wherein the accessed plurality of user communicationscomprises user communications generated during the determined timeperiod.
 5. The method of claim 1, wherein identifying the plurality ofsegments comprises performing a time analysis on the plurality of usercommunications.
 6. The method of claim 5, wherein performing the timeanalysis comprises: identifying two consecutive user communications ofthe plurality of user communications that were generated over a timeperiod exceeding a predetermined time period; and identifying twosegments of the plurality of segments corresponding to the twoidentified user communications.
 7. The method of claim 1, whereinidentifying the plurality of segments comprises performing a topicanalysis on the plurality of user communications.
 8. The method of claim7, wherein performing the topic analysis comprises: analyzing theplurality of user communications to identify a topic associated witheach user communication; and identifying two segments of the pluralityof segments based on a change of topics between two consecutive usercommunications, wherein each segment comprises user communications of asame topic.
 9. The method of claim 1, further comprising: updating theuser profile of the first user based on user behavior with respect toone or more previously generated personalized summaries.
 10. The methodof claim 1, wherein the first content source comprises an electroniccommunications inbox, a message thread, or a newsfeed.
 11. The method ofclaim 1, further comprising: ranking the generated personalizedsummaries based on the user interest scores associated with the selectedsegments corresponding to the respective personalized summary, whereinto present the personalized summaries comprises to present thepersonalized summaries based on their respective rankings.
 12. Themethod of claim 1, further comprising: generating a semantic embeddingfor each of the plurality of user communications, wherein eachpersonalized summary is generated by a machine-learning model in whichthe semantic embeddings representing the user communications associatedwith the one or more selected segments corresponding to the personalizesummary are used as inputs to the machine-learning model.
 13. The methodof claim 1, wherein the instructions to present the personalizedsummaries comprises instructions to present the personalized summariesas one or more of a text output, an audio output, or a visual output.14. One or more computer-readable non-transitory storage media embodyingsoftware that is operable when executed to: receive, by an assistantxbot associated with the one or more computing systems from a clientsystem associated with a first user, a request for a summary of usercommunications from a first content source; access a plurality of usercommunications from the first content source; identify a plurality ofsegments associated with the plurality of user communications, whereinthe plurality of segments is associated with a plurality of topics,respectively; select one or more of the segments for summarization;generate one or more personalized summaries of the one or more selectedsegments within a separate message thread between the assistant xbot andthe first user, wherein the personalization of the summary is based onthe user profile of the first user, and wherein each of the one or morepersonalized summaries comprises an interactive element that isselectable to respond to the respective segment; and send, to the clientsystem, instructions to present the personalized summaries to the firstuser responsive to the request.
 15. A system comprising: one or moreprocessors; and a non-transitory memory coupled to the processorscomprising instructions executable by the processors, the processorsoperable when executing the instructions to: receive, by an assistantxbot associated with the one or more computing systems from a clientsystem associated with a first user, a request for a summary of usercommunications from a first content source; access a plurality of usercommunications from the first content source; identify a plurality ofsegments associated with the plurality of user communications, whereinthe plurality of segments is associated with a plurality of topics,respectively; select one or more of the segments for summarization;generate one or more personalized summaries of the one or more selectedsegments within a separate message thread between the assistant xbot andthe first user, wherein the personalization of the summary is based onthe user profile of the first user, and wherein each of the one or morepersonalized summaries comprises an interactive element that isselectable to respond to the respective segment; and send, to the clientsystem, instructions to present the personalized summaries to the firstuser responsive to the request.